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X2Paddle
未验证 提交 0c93656b 编写于 作者: W WJJ1995 提交者： GitHub
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Support PyTorch InstanceNorm2d op (#638) 

* Add pytorch LeakyRelu op

* fix pytorch InstanceNorm op

* update paddle code format
上级 d5c2ceb3
隐藏空白更改
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Showing with 181 addition and 115 deletion
x2paddle/op_mapper/pytorch2paddle/aten.py
浏览文件 @ 0c93656b
......@@ -2637,7 +2637,7 @@ def aten_instance_norm(mapper, graph, node):
# 处理输入1，即%88
if inputs_name[1] in mapper.pytorch_params:
weights = mapper.pytorch_params[inputs_name[1]]
mapper.paddle_params[op_name + ".weight"] = weights
mapper.paddle_params[op_name + ".scale"] = weights
layer_attrs['num_features'] = weights.shape[0]
# 处理输入2，即%85
if inputs_name[2] in mapper.pytorch_params:
......@@ -2888,6 +2888,42 @@ def aten_leaky_relu_(mapper, graph, node):
return current_inputs, current_outputs
def aten_leaky_relu(mapper, graph, node):
""" 构造leaky relu激活的PaddleLayer。
TorchScript示例:
%input.117 : Tensor = aten::leaky_relu(%input.114, %1570)
参数含义:
%input.117 (Tensor): 输出，leaky relu后的结果。
%input.114 (Tensor): 需要leaky relu的Tensor。
%1570 (float): 输入中的元素小于0时的斜率。
"""
scope_name = mapper.normalize_scope_name(node)
op_name = name_generator("leakly_relu", mapper.nn_name2id)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [op_name, output_name]
layer_inputs = {}
layer_attrs = {}
inputs_name, inputs_node = mapper._get_inputs_name(node)
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
# 处理输入1，即%1570
layer_attrs["negative_slope"] = mapper.attrs[inputs_name[1]]
graph.add_layer(
"paddle.nn.LeakyReLU",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
def aten_len(mapper, graph, node):
""" 构造获取list长度的PaddleLayer。
TorchScript示例:
......
x2paddle/optimizer/pytorch_code_optimizer/hierachical_tree.py
浏览文件 @ 0c93656b
......@@ -13,13 +13,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os.path as osp
from treelib import Tree
from queue import Queue
from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import gen_layer_code, rename_layers, NN_KERNEL_WITH_PARAMS, NN_KERNEL_NAME
from x2paddle.optimizer.pytorch_code_optimizer.subgraphs_union import distinguish_sequential, get_inputs_outputs
from x2paddle.optimizer.pytorch_code_optimizer.subgraphs_union import distinguish_sequential, get_inputs_outputs
from x2paddle.core.program import PaddleLayer
from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode, PamareterTree
......@@ -29,21 +28,22 @@ SEPARATOR_IN_SCOPE = "/"
class HierarchicalTree(Tree):
""" 定义层次树。
"""
def __init__(self, pd_graph):
super(HierarchicalTree, self).__init__()
self.pd_graph = pd_graph
self.script = pd_graph.script
self.create_node("Module", self.pd_graph.name) # create root
self.create_node("Module", self.pd_graph.name) # create root
self._hierarchical_order = dict()
self.codes = list()
self.identifier_idx = dict()
self.param_tree = PamareterTree()
self.module_name2count = dict()
self.scope_name_list = list()
def insert(self, layer):
""" 往层次树中插入节点。
Args:
layer (PaddleLayer): 需要插入的节点。
"""
......@@ -56,12 +56,13 @@ class HierarchicalTree(Tree):
for input_layer_id in self.pd_graph.edges_in[layer_id]:
layer_id_list.append(int(input_layer_id))
layer_id_list = list(set(layer_id_list))
layer_id_list.sort(reverse=True)
layer_id_list.sort(reverse=True)
if layer.kernel == "prim.tuple":
for i, input_layer_id in enumerate(layer_id_list):
input_layer_id_str = str(input_layer_id)
scope_name = self.pd_graph.layers[input_layer_id_str].scope_name
scope_name = self.pd_graph.layers[
input_layer_id_str].scope_name
if i == 0:
min_scope_name = scope_name
else:
......@@ -73,30 +74,34 @@ class HierarchicalTree(Tree):
if len1 > len2:
min_scope_name = scope_name
if min_scope_name == "":
self.create_node(tag=layer.id,
identifier="no_scope_" + layer.id,
parent=self.pd_graph.name,
data=layer)
return
self.create_node(
tag=layer.id,
identifier="no_scope_" + layer.id,
parent=self.pd_graph.name,
data=layer)
return
layer.scope_name = min_scope_name
scope_name = min_scope_name
else:
for input_layer_id in layer_id_list:
input_layer_id_str = str(input_layer_id)
if self.pd_graph.layers[input_layer_id_str].scope_name != "":
scope_name = self.pd_graph.layers[input_layer_id_str].scope_name
break
if self.pd_graph.layers[
input_layer_id_str].scope_name != "":
scope_name = self.pd_graph.layers[
input_layer_id_str].scope_name
break
layer.scope_name = scope_name
else:
self.create_node(tag=layer.id,
identifier="no_scope_" + layer.id,
parent=self.pd_graph.name,
data=layer)
return
self.create_node(
tag=layer.id,
identifier="no_scope_" + layer.id,
parent=self.pd_graph.name,
data=layer)
return
scopes = scope_name.split(SEPARATOR_IN_SCOPE)
for idx, scope in enumerate(scopes):
parent = SEPARATOR_IN_SCOPE.join(scopes[:idx])#.lower()
identifier = SEPARATOR_IN_SCOPE.join(scopes[:idx + 1])#.lower()
parent = SEPARATOR_IN_SCOPE.join(scopes[:idx]) #.lower()
identifier = SEPARATOR_IN_SCOPE.join(scopes[:idx + 1]) #.lower()
if self.contains(identifier):
if idx != len(scopes) - 1:
parent_node = self.parent(identifier)
......@@ -109,11 +114,13 @@ class HierarchicalTree(Tree):
self.identifier_idx[identifier] = 0
else:
self.identifier_idx[identifier] += 1
identifier_name = identifier + SEPARATOR_IN_SCOPE + str(self.identifier_idx[identifier])
self.create_node(tag=scopes[idx],
identifier=identifier_name,
parent=identifier,
data=data)
identifier_name = identifier + SEPARATOR_IN_SCOPE + str(
self.identifier_idx[identifier])
self.create_node(
tag=scopes[idx],
identifier=identifier_name,
parent=identifier,
data=data)
data.scope_name = identifier_name
continue
else:
......@@ -125,23 +132,28 @@ class HierarchicalTree(Tree):
self.identifier_idx[identifier] = 0
else:
self.identifier_idx[identifier] += 1
self.create_node(tag=scopes[idx],
identifier=identifier + SEPARATOR_IN_SCOPE + str(self.identifier_idx[identifier]),
parent=identifier,
data=data)
self.create_node(
tag=scopes[idx],
identifier=identifier + SEPARATOR_IN_SCOPE +
str(self.identifier_idx[identifier]),
parent=identifier,
data=data)
self.identifier_idx[identifier] += 1
data = layer
self.create_node(tag=scopes[idx],
identifier=identifier + SEPARATOR_IN_SCOPE + str(self.identifier_idx[identifier]),
parent=identifier,
data=data)
self.create_node(
tag=scopes[idx],
identifier=identifier + SEPARATOR_IN_SCOPE +
str(self.identifier_idx[identifier]),
parent=identifier,
data=data)
continue
if idx == 0 and not self.contains(identifier):
data = layer if idx == len(scopes) - 1 else None
self.create_node(tag=scopes[idx],
identifier=identifier,
parent=self.pd_graph.name,
data=data)
self.create_node(
tag=scopes[idx],
identifier=identifier,
parent=self.pd_graph.name,
data=data)
else:
if idx == len(scopes) - 1:
if parent == "":
......@@ -153,7 +165,8 @@ class HierarchicalTree(Tree):
identifiers = list()
for child in childs:
child_identifier = child.identifier
if child_identifier.startswith(prefix) and child_identifier != prefix:
if child_identifier.startswith(
prefix) and child_identifier != prefix:
identifiers.append(child_identifier)
if len(identifiers) == 0:
identifier = prefix + "_0"
......@@ -162,14 +175,15 @@ class HierarchicalTree(Tree):
for id_obj in identifiers:
identifier_ids.append(int(id_obj.split("_")[-1]))
identifier_ids.sort()
identifier = prefix + "_{}".format(identifier_ids[-1] + 1)
identifier = prefix + "_{}".format(identifier_ids[-1] +
1)
data = layer if idx == len(scopes) - 1 else None
self.create_node(tag=scopes[idx],
identifier=identifier,
parent=parent,
data=data)
self.create_node(
tag=scopes[idx],
identifier=identifier,
parent=parent,
data=data)
def update_hierarchical_order(self):
""" 更新层次排序，使用一个字典存储该信息，
关键字为当前层次，值为节点名字。
......@@ -201,32 +215,36 @@ class HierarchicalTree(Tree):
diff_attrs_column.append(column)
break
return diff_attrs_column
def merge_node(self, sub_layers_list, attrs_table, node_name2sub_layers, module_name):
def merge_node(self, sub_layers_list, attrs_table, node_name2sub_layers,
module_name):
""" 将一个scope的节点合成一个Module（Class），并将对应的Class代码
放到code字符串中。
"""
def get_node_name(sub_layers):
for k, v in node_name2sub_layers.items():
if v == sub_layers:
node_name = k
break
return node_name
sub_layers = sub_layers_list[0]
node_name = get_node_name(sub_layers)
sub_layers, _, _ = rename_layers(sub_layers)
diff_attrs_column = self.analyze_attrs_table(attrs_table)
if module_name is None:
module_name = node_name.replace("/", "_") #node_name.split("/")[-1]
module_name = node_name.replace("/", "_") #node_name.split("/")[-1]
module_name = module_name[0].upper() + module_name[1:]
if module_name in self.module_name2count:
module_name = module_name + "_0"
code_str = gen_layer_code(self.pd_graph, sub_layers, module_name,
different_attrs=diff_attrs_column)
code_str = gen_layer_code(
self.pd_graph,
sub_layers,
module_name,
different_attrs=diff_attrs_column)
self.codes.append(code_str)
for sub_layers in sub_layers_list:
inputs, outputs = get_inputs_outputs(self.pd_graph, sub_layers)
......@@ -241,23 +259,26 @@ class HierarchicalTree(Tree):
mn = module_name.lower() + "__"
else:
mn = module_name.lower()
outputs = ["{}/{}".format(mn, self.module_name2count[module_name])] + outputs
outputs = [
"{}/{}".format(mn, self.module_name2count[module_name])
] + outputs
node_name = get_node_name(sub_layers)
diff_attrs = dict()
diff_attrs = dict()
for column in diff_attrs_column:
diff_attrs[column] = attrs_table.get(column).loc[node_name]
node_name_seg = node_name.split(SEPARATOR_IN_SCOPE)
node_name_seg[-1] = module_name.lower()
new_node_name = SEPARATOR_IN_SCOPE.join(node_name_seg)
new_layer = PaddleLayer(id=list(sub_layers.keys())[-1],
kernel="module",
inputs=inputs_dict,
outputs=outputs,
scope_name=new_node_name,
module=module_name,
**diff_attrs)
new_layer = PaddleLayer(
id=list(sub_layers.keys())[-1],
kernel="module",
inputs=inputs_dict,
outputs=outputs,
scope_name=new_node_name,
module=module_name,
**diff_attrs)
_, nn_param_nodes, _ = rename_layers(sub_layers, self.param_tree)
param_node = PamareterNode(old_name=outputs[0])
for node in nn_param_nodes:
......@@ -272,28 +293,26 @@ class HierarchicalTree(Tree):
self.pd_graph.build()
self[node_name].data = new_layer
def find_subgraph_diff(self, module_name2sub_layers, module_name2sub_identifiers, node_name2sub_layers, name):
def find_subgraph_diff(self, module_name2sub_layers,
module_name2sub_identifiers, node_name2sub_layers,
name):
""" 查找子图的diff，主要是输入参数的diff。
"""
sub_layers = module_name2sub_layers[name]
sub_identifiers = module_name2sub_identifiers[name]
new_sub_layers, new_sub_sequentials, sequentials2attrs_table = distinguish_sequential(self.pd_graph,
name,
sub_layers,
sub_identifiers,
node_name2sub_layers)
new_sub_layers, new_sub_sequentials, sequentials2attrs_table = distinguish_sequential(
self.pd_graph, name, sub_layers, sub_identifiers,
node_name2sub_layers)
module_name2sub_layers.pop(name)
module_name2sub_identifiers.pop(name)
for k, v in new_sub_layers.items():
module_name2sub_layers[k] = v
module_name2sub_identifiers[k] = new_sub_sequentials[k]
return sequentials2attrs_table
def convert_subgraph_to_layer(self):
"""
"""
1. 根据_hierarchical_order，从最深的层次开始将
子图合并成layer（即合成节点）。
2. 根据参数名新旧对应关系，更新参数名。
......@@ -314,14 +333,16 @@ class HierarchicalTree(Tree):
sub_layers = dict()
sub_identifiers = dict()
for successor_name in node_inst.successors(self.identifier):
sub_layers[self[successor_name].data.id] = self[successor_name].data
sub_identifiers[self[successor_name].data.id] = self[successor_name].data.scope_name.split("/")[-1]
sub_layers[self[successor_name].data.id] = self[
successor_name].data
sub_identifiers[self[successor_name].data.id] = self[
successor_name].data.scope_name.split("/")[-1]
node_name2sub_layers[node_name] = sub_layers
node_name_segs = node_name.split("/")
# 获取Module的名字
module = self.script
is_largest_module = False # 当前module是否是最外层的Module
is_largest_module = False # 当前module是否是最外层的Module
for name_id, name in enumerate(node_name_segs):
name = name.split("__")[0]
if not hasattr(module, name):
......@@ -345,23 +366,29 @@ class HierarchicalTree(Tree):
len(module_name2sub_layers[module_name][0][list(module_name2sub_layers[module_name][0].keys())[-1]].outputs):
break
if module_name not in module_name2sub_layers:
module_name2sub_layers[module_name] = [sub_layers]
module_name2sub_identifiers[module_name] = [sub_identifiers]
module_name2sub_layers[
module_name] = [sub_layers]
module_name2sub_identifiers[
module_name] = [sub_identifiers]
else:
module_name2sub_layers[module_name].append(sub_layers)
module_name2sub_identifiers[module_name].append(sub_identifiers)
module_name2sub_layers[module_name].append(
sub_layers)
module_name2sub_identifiers[module_name].append(
sub_identifiers)
else:
module_name2sub_layers[module_name].append(sub_layers)
module_name2sub_identifiers[module_name].append(sub_identifiers)
module_name2sub_layers[module_name].append(
sub_layers)
module_name2sub_identifiers[module_name].append(
sub_identifiers)
else:
module_name2sub_layers[module_name] = [sub_layers]
module_name2sub_identifiers[module_name] = [sub_identifiers]
module_name2sub_identifiers[
module_name] = [sub_identifiers]
module_names = list(module_name2sub_layers.keys())
for module_name in module_names:
sequentials2attrs_table = self.find_subgraph_diff(module_name2sub_layers,
module_name2sub_identifiers,
node_name2sub_layers,
module_name)
sequentials2attrs_table = self.find_subgraph_diff(
module_name2sub_layers, module_name2sub_identifiers,
node_name2sub_layers, module_name)
for name in sequentials2attrs_table.keys():
if name.startswith("Sequential"):
# 若Module的名字为Sequential，则以scope_name的名字来命名，在merge_node中实现
......@@ -371,54 +398,57 @@ class HierarchicalTree(Tree):
while module_name in current_module_name_list:
module_name += "__0"
current_module_name_list.append(module_name)
self.merge_node(module_name2sub_layers[name],
sequentials2attrs_table[name],
node_name2sub_layers,
module_name)
self.merge_node(module_name2sub_layers[name],
sequentials2attrs_table[name],
node_name2sub_layers, module_name)
def update_parameters(self):
""" 更新参数。
"""
self.param_tree.traverse()
full_old_name_list = copy.deepcopy(list(self.pd_graph.parameters.keys()))
full_old_name_list = copy.deepcopy(
list(self.pd_graph.parameters.keys()))
for old_name, new_name in self.param_tree.old2new.items():
for full_old_name in full_old_name_list:
if full_old_name.startswith("{}.".format(old_name)):
full_new_name = full_old_name.replace("{}.".format(old_name), "{}.".format(new_name))
full_new_name = full_old_name.replace(
"{}.".format(old_name), "{}.".format(new_name))
params = self.pd_graph.parameters.pop(full_old_name)
self.pd_graph.parameters[full_new_name] = params
if full_old_name == old_name:
full_new_name = full_old_name.replace(old_name, new_name)
params = self.pd_graph.parameters.pop(full_old_name)
self.pd_graph.parameters[full_new_name] = params
def save_source_files(self, save_dir):
def gen_main_code():
input_data_name = ', '.join(self.pd_graph.inputs)
run_func_list = list()
run_func_list.append("def main({}):".format(input_data_name))
run_func_list.append(" # There are {} inputs.".format(len(self.pd_graph.inputs_info)))
run_func_list.append(" # There are {} inputs.".format(
len(self.pd_graph.inputs_info)))
for k, v in self.pd_graph.inputs_info.items():
run_func_list.append(" # {}: shape-{}, type-{}.".format(k, v[0], v[1]))
run_func_list.extend(
[" paddle.disable_static()",
" params = paddle.load('{}')".format(osp.join(osp.abspath(save_dir), "model.pdparams")),
" model = {}()".format(self.pd_graph.name),
" model.set_dict(params)",
" model.eval()",
" out = model({})".format(input_data_name),
" return out"])
run_func_list.append(" # {}: shape-{}, type-{}.".format(k, v[
0], v[1]))
run_func_list.extend([
" paddle.disable_static()",
" params = paddle.load('{}')".format(
osp.join(osp.abspath(save_dir), "model.pdparams")),
" model = {}()".format(self.pd_graph.name),
" model.set_dict(params)", " model.eval()",
" out = model({})".format(input_data_name), " return out"
])
return "\n".join(run_func_list)
self.update_hierarchical_order()
self.convert_subgraph_to_layer()
self.update_parameters()
import_list = ["import paddle",
"import math",
"from x2paddle.op_mapper.pytorch2paddle " + \
"import pytorch_custom_layer as x2paddle_nn"
"\n",]
import_str = "\n".join(import_list)
"import pytorch_custom_layer as x2paddle_nn",
"",]
import_str = "\n".join(import_list) + "\n"
if not osp.exists(save_dir):
os.makedirs(save_dir)
f = open(osp.join(save_dir, 'x2paddle_code.py'), 'w')
......
x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py
浏览文件 @ 0c93656b
......@@ -263,7 +263,7 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()):
layer.kernel.startswith("custom_layer"):
line = "self.{}".format(layer.outputs[0])
if layer.kernel.startswith("custom_layer"):
line += "= x2paddle_nn.{}(".format(layer.kernel.split(":")[-1])
line += " = x2paddle_nn.{}(".format(layer.kernel.split(":")[-1])
else:
line += " = {}(".format(layer.kernel)
for k, v in layer.attrs.items():
......
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