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X2Paddle
提交 e2a1d944 编写于 作者: S SunAhong1993
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add reshape opt and add aten

上级 e3b6e8a0
隐藏空白更改
内联 并排
Showing with 579 addition and 113 deletion
x2paddle/op_mapper/pytorch2paddle/aten.py
浏览文件 @ e2a1d944
......@@ -13,6 +13,7 @@
# limitations under the License.
from x2paddle.core.util import *
from x2paddle.core.program import PaddleGraph
dtype_dict = {
0: string("uint8"),
......@@ -833,7 +834,8 @@ def aten_dim(mapper, graph, node):
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.shape", inputs=layer_inputs, outputs=layer_outputs)
graph.add_layer(
"fluid.layers.shape", inputs=layer_inputs, outputs=layer_outputs)
graph.add_layer(
"prim.len", inputs={"input": output_name}, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -1027,12 +1029,15 @@ def aten_eq(mapper, graph, node):
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
x_value = list(node.inputs())[0]
x_type = x_value.type()
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["y"] = inputs_name[1]
y_value = list(node.inputs())[1]
y_type = y_value.type()
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.eq", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -1064,13 +1069,14 @@ def aten_exp(mapper, graph, node):
def aten_expand(mapper, graph, node):
""" 构造复制维度的PaddleLayer。
""" 构造对某维度进行广播的PaddleLayer。
TorchScript示例:
%1889 : Tensor = aten::expand(%1875, %1888, %1567)
参数含义:
%1889 (Tensor): 复制后的结果。
%1875 (Tensor): 需要复制的Tensor。
%1889 (Tensor): 广播后的结果。
%1875 (Tensor): 需要广播的Tensor。
%1888 (int): 广播的维度。
%1567 (bool): 未使用。
"""
output_name = mapper._get_outputs_name(node)[0]
......@@ -1084,13 +1090,54 @@ def aten_expand(mapper, graph, node):
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%1888
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.str",
inputs={"input": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_cond"],
y=string("VarType.BOOL"))
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if1"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"fluid.layers.cast",
inputs={"x": inputs_name[0]},
outputs=[inputs_name[0]],
dtype=string("int64"))
block.add_layer(
"fluid.layers.create_global_var",
inputs={"shape": inputs_name[1]},
outputs=[inputs_name[1] + "_var"],
value=1.0,
dtype=string("int64"),
persistable=True)
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"])
block.add_layer(
"fluid.layers.create_global_var",
inputs={"shape": inputs_name[1]},
outputs=[inputs_name[1] + "_var"],
value=1.0,
dtype=inputs_name[0] + "_type",
persistable=True)
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
if_layer.inputs["input-1"] = inputs_name[1]
layer_inputs["target_tensor"] = inputs_name[1] + "_var"
current_outputs.append(inputs_name[1] + "_var")
# 获取当前节点输入的list
......@@ -1102,6 +1149,82 @@ def aten_expand(mapper, graph, node):
return current_inputs, current_outputs
def aten_expand_as(mapper, graph, node):
""" 构造广播的PaddleLayer。
TorchScript示例:
%1889 : Tensor = aten::expand_as(%1875, %1888)
参数含义:
%1889 (Tensor): 广播后的结果。
%1875 (Tensor): 需要广播的Tensor。
%1888 (Tensor): 广播的示例。
"""
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
layer_inputs = {}
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%1875
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%1888
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["target_tensor"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.str",
inputs={"input": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_cond"],
y=string("VarType.BOOL"))
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if1"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"prim.type",
inputs={"input": inputs_name[1]},
outputs=[inputs_name[1] + "_type"])
block.add_layer(
"fluid.layers.cast",
inputs={"x": inputs_name[0]},
outputs=[inputs_name[0]],
dtype=inputs_name[1] + "_type")
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
if_layer.inputs["input-1"] = inputs_name[1]
graph.add_layer(
"fluid.layers.expand_as", inputs=layer_inputs, outputs=layer_outputs)
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if2"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"fluid.layers.cast",
inputs={"x": layer_outputs[0]},
outputs=layer_outputs,
dtype=string("bool"))
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = layer_outputs[0]
return current_inputs, current_outputs
def aten_eye(mapper, graph, node):
""" 构造批次二维矩阵的PaddleLayer。
......@@ -1734,6 +1857,101 @@ def aten_lt(mapper, graph, node):
return current_inputs, current_outputs
def aten_masked_fill_(mapper, graph, node):
""" 构造填充mask的PaddleLayer。
TorchScript示例:
%input.4 : Tensor = aten::masked_fill_(%scores.2, %mask.2, %46)
参数含义:
%input.4 (Tensor): 输出，填充后的结果。
%scores.2 (Tensor): 需要填充的Tensor。
%mask.2 (Tensor): bool型的Tensor，哪些位置需要填充。
%46 (-): 填充的值。
"""
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
layer_inputs = {}
inputs_name, inputs_node = mapper._get_inputs_name(node)
# 获取当前节点输入的list
current_inputs = []
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.4
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
current_inputs.append(inputs_name[0])
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"])
# 处理输入1，即%scores.2
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.logical_not",
inputs={"x": inputs_name[1]},
outputs=[inputs_name[1] + "_not"])
graph.add_layer(
"fluid.layers.cast",
inputs={"x": inputs_name[1]},
outputs=[inputs_name[1] + "_mask"],
dtype=inputs_name[0] + "_type")
graph.add_layer(
"fluid.layers.cast",
inputs={"x": inputs_name[1] + "_not"},
outputs=[inputs_name[1] + "_not_mask"],
dtype=inputs_name[0] + "_type")
graph.add_layer(
"paddle.multiply",
inputs={"x": inputs_name[0],
"y": inputs_name[1] + "_not_mask"},
outputs=[inputs_name[0] + "_not_mask"])
# 处理输入2，即%46
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs)
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[2]},
outputs=[inputs_name[2] + "_cond1"],
y="-float('inf')")
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[2]},
outputs=[inputs_name[2] + "_cond2"],
y="float('inf')")
graph.add_layer(
"prim.or",
inputs={
"x": inputs_name[2] + "_cond1",
"y": inputs_name[2] + "_cond2"
},
outputs=[inputs_name[2] + "_cond"])
graph.add_layer(
"prim.if", {'input': inputs_name[2] + "_cond"},
outputs=[inputs_name[2] + "_if"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"prim.equal",
inputs={"input": inputs_name[1] + "_mask"},
outputs=[inputs_name[2] + "_1"])
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"prim.mul",
inputs={"x": inputs_name[1] + "_mask",
"y": inputs_name[2]},
outputs=[inputs_name[2] + "_1"])
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[1] + "_mask"
if_layer.inputs["input-1"] = inputs_name[2]
if_layer.outputs.append(inputs_name[2] + "_1")
graph.add_layer(
"fluid.layers.elementwise_add",
inputs={"x": inputs_name[2] + "_1",
"y": inputs_name[0] + "_not_mask"},
outputs=layer_outputs)
return current_inputs, current_outputs
def aten_max_pool2d(mapper, graph, node):
""" 构造最大池化的PaddleLayer。
......@@ -2184,11 +2402,52 @@ def aten_reshape(mapper, graph, node):
layer_inputs["shape"] = inputs_name[1]
current_inputs.append(inputs_name[1])
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.str",
inputs={"input": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_cond"],
y=string("VarType.BOOL"))
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if1"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"fluid.layers.cast",
inputs={"x": inputs_name[0]},
outputs=[inputs_name[0]],
dtype=string("int32"))
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
graph.add_layer(
"fluid.layers.reshape",
inputs=layer_inputs,
outputs=layer_outputs,
**layer_attrs)
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if2"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"fluid.layers.cast",
inputs={"x": layer_outputs[0]},
outputs=layer_outputs,
dtype=string("bool"))
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = layer_outputs[0]
return current_inputs, current_outputs
......@@ -2416,7 +2675,8 @@ def aten_size(mapper, graph, node):
**layer_attrs)
return current_inputs, current_outputs
graph.add_layer("prim.shape", inputs=layer_inputs, outputs=layer_outputs)
graph.add_layer(
"fluid.layers.shape", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -2465,7 +2725,7 @@ def aten_slice(mapper, graph, node):
layer_inputs["axes"] = inputs_name[1] + "_list"
current_inputs.append(inputs_name[1] + "_list")
current_outputs.append(inputs_name[1] + "_list")
# 处理输入3，即%82
# 处理输入2，即%82
if inputs_name[2] in mapper.attrs:
graph.add_layer(
"prim.list",
......@@ -2485,14 +2745,11 @@ def aten_slice(mapper, graph, node):
current_outputs.append(inputs_name[2] + "_list")
# 处理输入3，即%85
if inputs_name[3] in mapper.attrs:
if 9223372036854775807 == mapper.attrs[inputs_name[3]]:
import math
input0 = int(math.pow(2, 31) - 1)
graph.add_layer(
"prim.list",
inputs={},
outputs=[inputs_name[3] + "_list"],
input0=input0)
input0=mapper.attrs[inputs_name[3]])
else:
mapper._check_input(graph, inputs_node[3], inputs_name[3],
current_outputs)
......@@ -2749,27 +3006,7 @@ def aten_split(mapper, graph, node):
if "[]" in str(input_type):
layer_inputs["num_or_sections"] = inputs_name[1]
else:
graph.add_layer(
"prim.shape",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[1] + "_shape"])
graph.add_layer(
"prim.getitem",
inputs={
"list": inputs_name[1] + "_shape",
"index": inputs_name[2]
},
outputs=[inputs_name[1] + "_item"])
graph.add_layer(
"prim.div",
inputs={"x": inputs_name[1] + "_item",
"y": inputs_name[1]},
outputs=[inputs_name[1] + "_div"])
graph.add_layer(
"prim.int",
inputs={"input": inputs_name[1] + "_div"},
outputs=[inputs_name[1] + "_int"])
layer_inputs["num_or_sections"] = inputs_name[1] + "_int"
layer_attrs["num_or_sections"] = 1
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -2849,7 +3086,7 @@ def aten_transpose(mapper, graph, node):
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.shape",
"fluid.layers.shape",
inputs={"input": inputs_name[0]},
outputs=[output_name + "_shape"])
current_outputs.append(output_name + "_shape")
......@@ -3071,12 +3308,52 @@ def aten_view(mapper, graph, node):
current_outputs)
layer_inputs["shape"] = inputs_name[1]
current_inputs.append(inputs_name[1])
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.str",
inputs={"input": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_type"])
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_cond"],
y=string("VarType.BOOL"))
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if1"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"fluid.layers.cast",
inputs={"x": inputs_name[0]},
outputs=[inputs_name[0]],
dtype=string("int32"))
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
graph.add_layer(
"fluid.layers.reshape",
inputs=layer_inputs,
outputs=layer_outputs,
**layer_attrs)
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if2"])
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph")
block.add_layer(
"fluid.layers.cast",
inputs={"x": layer_outputs[0]},
outputs=layer_outputs,
dtype=string("bool"))
if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = layer_outputs[0]
return current_inputs, current_outputs
......
x2paddle/op_mapper/pytorch2paddle/prim.py
浏览文件 @ e2a1d944
......@@ -33,7 +33,18 @@ def prim_Constant(mapper, graph, node):
if isinstance(value, str):
value = string(value)
if str(output_type) == "Tensor":
value = "paddle.to_tensor({})".format(value)
# value = "paddle.to_tensor({})".format(value)
value = "{}".format(value)
if "inf" in str(value):
t = str(type(value)).split("'")[1]
if str(value).startswith("-"):
value = "-{}({})".format(t, string(str(value)[1:]))
else:
value = "{}({})".format(t, string(str(value)))
if "9223372036854775807" in str(value):
import math
value = int(math.pow(2, 31) - 1)
graph.add_layer(
"prim.constant", inputs={}, outputs=[output_name], value=value)
return [], [output_name]
......@@ -193,6 +204,7 @@ def prim_ListUnpack(mapper, graph, node):
graph.add_layer(
"prim.list_unpack", inputs=layer_inputs, outputs=layer_outputs)
mapper.split_len[list(layer_inputs.values())[0]] = len(layer_outputs)
return current_inputs, current_outputs
......@@ -302,19 +314,25 @@ def prim_NumToTensor(mapper, graph, node):
current_outputs = [output_name]
# 处理输入0，即%86
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["value"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
input_type = list(node.inputs())[0].type()
layer_attrs["dtype"] = input_type
layer_attrs["persistable"] = True
layer_attrs["shape"] = [1]
graph.add_layer(
"fluid.layers.create_global_var",
inputs=layer_inputs,
outputs=layer_outputs,
**layer_attrs)
if inputs_node[0].kind() == "aten::size":
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim_equal", inputs=layer_inputs, outputs=layer_outputs)
else:
layer_inputs["value"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
input_type = list(node.inputs())[0].type()
layer_attrs["dtype"] = input_type
layer_attrs["persistable"] = True
layer_attrs["shape"] = [1]
graph.add_layer(
"fluid.layers.create_global_var",
inputs=layer_inputs,
outputs=layer_outputs,
**layer_attrs)
return current_inputs, current_outputs
......
x2paddle/op_mapper/pytorch2paddle/prim2code.py
浏览文件 @ e2a1d944
......@@ -257,8 +257,6 @@ def prim_mul(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = {} * {}".format(layer.outputs[0],
get_value(layer, "x"), get_value(layer, "y"))
forward_func.extend(gen_codes([line], indent=indent))
if "x2589" in layer.outputs:
print(layer.inputs["y"])
def prim_ne(layer, indent=1, init_func=[], forward_func=[]):
......@@ -277,6 +275,12 @@ def prim_not(layer, indent=1, init_func=[], forward_func=[]):
forward_func.extend(gen_codes([line], indent=indent))
def prim_or(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = {} or {}".format(layer.outputs[0],
get_value(layer, "x"), get_value(layer, "y"))
forward_func.extend(gen_codes([line], indent=indent))
def prim_replaceitem(layer, indent=1, init_func=[], forward_func=[]):
line = "{}[{}] = {}".format(
get_value(layer, "list"),
......@@ -318,15 +322,10 @@ def prim_set_item(layer, indent=1, init_func=[], forward_func=[]):
forward_func.extend(gen_codes([line], indent=indent))
def prim_shape(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = {}.shape".format(layer.outputs[0], get_value(layer, "input"))
forward_func.extend(gen_codes([line], indent=indent))
def prim_shape_dim(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = {}.shape[{}]".format(layer.outputs[0],
get_value(layer, "input"),
get_value(layer, "dim"))
line = "{} = fluid.layers.shape({})[{}]".format(layer.outputs[0],
get_value(layer, "input"),
get_value(layer, "dim"))
forward_func.extend(gen_codes([line], indent=indent))
......@@ -339,6 +338,11 @@ def prim_slice(layer, indent=1, init_func=[], forward_func=[]):
forward_func.extend(gen_codes([line], indent=indent))
def prim_str(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = str({})".format(layer.outputs[0], get_value(layer, "input"))
forward_func.extend(gen_codes([line], indent=indent))
def prim_sub(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = {} - {}".format(layer.outputs[0],
get_value(layer, "x"), get_value(layer, "y"))
......@@ -361,6 +365,11 @@ def prim_tuple_unpack(layer, indent=1, init_func=[], forward_func=[]):
forward_func.extend(gen_codes([line], indent=indent))
def prim_type(layer, indent=1, init_func=[], forward_func=[]):
line = "{} = {}.dtype".format(layer.outputs[0], get_value(layer, "input"))
forward_func.extend(gen_codes([line], indent=indent))
def prim_warnings(layer, indent=1, init_func=[], forward_func=[]):
lines = ["import warnings"]
line = "warnings.warn({}, stacklevel={})".format(
......
x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py
浏览文件 @ e2a1d944
......@@ -31,6 +31,7 @@ class PyTorchOpMapper(OpMapper):
self.attrs = {} # key为节点名，value为属性值
self.output_index = 0
self.dygraph_name_id = {} # 动态图__init__输出名字中的id，key为kernel类型，value为id
self.split_len = {} # split的长度
# 转换
self.check_op(decoder.graph)
self.graph, _ = self.traverse(decoder.graph)
......@@ -116,6 +117,11 @@ class PyTorchOpMapper(OpMapper):
inputs_name, inputs_node = self._get_inputs_name(
script_graph.return_node())
graph.outputs = inputs_name
# 更新split参数
for layer in graph.layers.values():
if layer.kernel == "fluid.layers.split" and "num_or_sections" in layer.attrs:
layer.attrs["num_or_sections"] = self.split_len[layer.outputs[
0]]
return graph, graph_inputs
def _get_outputs_name(self, node, attr_name=None):
......
x2paddle/optimizer/fusion/__init__.py
浏览文件 @ e2a1d944
......@@ -22,3 +22,5 @@ from .fc_fuser import FcFuser
from .fc_fuse_pass import FcFusePass
from .interpolate_bilinear_fuser import InterpolateBilinearFuser
from .interpolate_bilinear_fuse_pass import InterpolateBilinearFusePass
from .reshape_fuser import ReshapeFuser
from .reshape_fuse_pass import ReshapeFusePass
x2paddle/optimizer/fusion/adaptive_pool2d_fuser.py
浏览文件 @ e2a1d944
......@@ -26,19 +26,13 @@ class AdaptivePool2dFuser(FuseBase):
""" 描述需要替换的adaptive pool2d图结构。
adaptive pool2d层模式python实现代码示例:
x72 = [6, 6]
x73 = x71.shape
x75 = 'Exception'
x76 = 9223372036854775807
x77 = 1
x73 = fluid.layers.shape(x71)
x78 = len(x73)
x79 = 2
x80 = x78 <= x79
if x80 :
raise RaiseException(x75)
x83 = []
x84 = -2
x85 = x73[x84: x76: x77]
x86 = 2
x87 = len(x85)
x88 = [x86, x87]
x89 = min(x88)
......@@ -54,62 +48,42 @@ class AdaptivePool2dFuser(FuseBase):
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(0)], value=[6, 6])
self.pattern.add_layer(
"prim.shape",
"fluid.layers.shape",
inputs={'input': "pool-input-0"},
outputs=[gen_name(1)])
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(2)], value=True)
self.pattern.add_layer(
"prim.constant",
inputs={},
outputs=[gen_name(3)],
value="Exception")
self.pattern.add_layer(
"prim.constant",
inputs={},
outputs=[gen_name(4)],
value=9223372036854775807)
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(5)], value=1)
self.pattern.add_layer(
"prim.len", inputs={"input": gen_name(1)}, outputs=[gen_name(6)])
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(7)], value=2)
self.pattern.add_layer(
"prim.le",
inputs={"x": gen_name(6),
"y": gen_name(7)},
"y": "pool-input-1"},
outputs=[gen_name(8)])
self.pattern.add_layer("prim.if", {'input': gen_name(8)}, [gen_name(9)])
if_layer = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(if_layer, graph_type="dygraph")
pattern_block0.add_layer(
"prim.exception",
inputs={"input": gen_name(3)},
inputs={"input": "pool-input-6"},
outputs=[gen_name(9)])
if_layer.inputs["input-0"] = gen_name(3)
if_layer.inputs["input-0"] = "pool-input-6"
if_layer.add_block(pattern_block0)
pattern_block1 = PaddleGraph(if_layer, graph_type="dygraph")
if_layer.add_block(pattern_block1)
self.pattern.add_layer("prim.list", inputs={}, outputs=[gen_name(10)])
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(11)], value=-2)
self.pattern.add_layer(
"prim.slice",
inputs={
"input": gen_name(1),
"start": gen_name(11),
"end": gen_name(4),
"step": gen_name(5)
"start": "pool-input-2",
"end": "pool-input-3",
"step": "pool-input-4"
},
outputs=[gen_name(12)])
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(13)], value=2)
self.pattern.add_layer(
"prim.len", inputs={"input": gen_name(12)}, outputs=[gen_name(14)])
self.pattern.add_layer(
"prim.list",
inputs={"input0": gen_name(13),
inputs={"input0": "pool-input-4",
"input1": gen_name(14)},
outputs=[gen_name(15)])
self.pattern.add_layer(
......@@ -138,7 +112,15 @@ class AdaptivePool2dFuser(FuseBase):
"pool_size": gen_name(10)},
outputs=[gen_name(21)],
**pool_attrs)
self.pattern.build(inputs={"input-0": "pool-input-0"})
self.pattern.build(inputs={
"input-0": "pool-input-0",
"input-1": "pool-input-1",
"input-2": "pool-input-2",
"input-3": "pool-input-3",
"input-4": "pool-input-4",
"input-5": "pool-input-5",
"input-6": "pool-input-6"
})
def insert_new_layer(self, graph, parameters, matches):
parameters = graph.parameters
......
x2paddle/optimizer/fusion/fc_fuser.py
浏览文件 @ e2a1d944
......@@ -26,7 +26,6 @@ class FcFuser(FuseBase):
def build_pattern(self):
""" 描述需要替换的fc图结构。
fc层模式python实现代码示例:
x131 = 2
x133 = x128.shape
x133 = len(x133)
x134 = x133 == x131
......@@ -49,18 +48,15 @@ class FcFuser(FuseBase):
return "x" + str(id)
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(0)], value=2)
self.pattern.add_layer(
"prim.constant", inputs={}, outputs=[gen_name(1)], value=1)
self.pattern.add_layer(
"prim.shape", inputs={'input': "fc-input-0"},
"fluid.layers.shape",
inputs={'input': "fc-input-0"},
outputs=[gen_name(2)])
self.pattern.add_layer(
"prim.len", inputs={'input': gen_name(2)}, outputs=[gen_name(2)])
self.pattern.add_layer(
"prim.eq",
inputs={"eq0": gen_name(2),
"eq1": gen_name(0)},
"eq1": "fc-input-1"},
outputs=[gen_name(3)])
self.pattern.add_layer("prim.if", {'input': gen_name(3)}, [gen_name(4)])
self.pattern.outputs.append(gen_name(4))
......@@ -106,7 +102,7 @@ class FcFuser(FuseBase):
outputs=[gen_name(6)],
perm=[1, 0])
pattern_block1.add_layer(
"fluid.layers.matmul",
"paddle.matmul",
inputs={"x": "fc-input-0",
"y": gen_name(6)},
outputs=[gen_name(9)])
......@@ -126,7 +122,9 @@ class FcFuser(FuseBase):
"prim.equal", inputs={'input': gen_name(13)},
outputs=[gen_name(4)])
if_layer1.add_block(pattern_block1)
self.pattern.build(inputs={"input-0": "fc-input-0"})
self.pattern.build(
inputs={"input-0": "fc-input-0",
"input-1": "fc-input-1"})
def insert_new_layer(self, graph, parameters, matches):
new_layer = self.gen_new_layer(parameters, matches)
......@@ -136,13 +134,13 @@ class FcFuser(FuseBase):
def gen_new_layer(self, parameters, matches):
layers_id = list(matches.keys())
layer = matches[layers_id[2]]
layer = matches[layers_id[0]]
input_name = layer.inputs["input"]
layer = matches[layers_id[5]]
layer = matches[layers_id[3]]
output_name = layer.outputs[0]
layer = matches[layers_id[6]]
layer = matches[layers_id[4]]
weight_name = layer.attrs["value"][8:-2]
layer = matches[layers_id[8]]
layer = matches[layers_id[6]]
bias_name = layer.attrs["value"][8:-2]
attrs = dict()
attrs["input_dim"] = parameters[weight_name].shape[1]
......
x2paddle/optimizer/fusion/reshape_fuse_pass.py 0 → 100644
浏览文件 @ e2a1d944
# 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 import ReshapeFuser
from x2paddle.optimizer.pass_manager import pass_register
@pass_register
class ReshapeFusePass(Pass):
name = "reshape_fuse_pass"
def __init__(self):
Pass.__init__(self)
def apply(self, graph):
fuser = ReshapeFuser()
fuser.operate(graph, match_kind="edge")
# 用于注册
reshape_fuse_pass = ReshapeFusePass()
x2paddle/optimizer/fusion/reshape_fuser.py 0 → 100644
浏览文件 @ e2a1d944
# 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 numpy as np
from x2paddle.optimizer.pattern_matcher import FuseBase
from x2paddle.core.program import PaddleGraph, PaddleLayer
from x2paddle.core.util import *
class ReshapeFuser(FuseBase):
def __init__(self):
super(ReshapeFuser, self).__init__(graph_type="dygraph")
def build_pattern(self):
""" 描述需要替换的reshape图结构。
reshape层模式python实现代码示例:
x165 = int(x164)
x166 = [x158, x159, x165]
x167 = fluid.layers.reshape(x=x157, shape=x166)
"""
def gen_name(id):
return "x" + str(id)
self.pattern.add_layer(
"prim.int",
inputs={"input": "reshape-input-0"},
outputs=[gen_name(0)])
self.pattern.add_layer(
"prim.list",
inputs={
"input0": "reshape-input-1",
"input1": "reshape-input-2",
"input2": gen_name(0)
},
outputs=[gen_name(1)])
self.pattern.add_layer(
"fluid.layers.reshape",
inputs={"x": "reshape-input-3",
"shape": gen_name(1)},
outputs=[gen_name(2)])
self.pattern.build(inputs={
"input-0": "reshape-input-0",
"input-1": "reshape-input-1",
"input-2": "reshape-input-2",
"input-3": "reshape-input-3",
})
def insert_new_layer(self, graph, parameters, matches):
self.update_layer(matches)
matches.pop(list(matches.keys())[1])
matches.pop(list(matches.keys())[1])
def update_layer(self, matches):
layers_id = list(matches.keys())
layer = matches[layers_id[0]]
int_input_name = layer.inputs["input"]
output_name = layer.outputs[0]
layer = matches[layers_id[1]]
for key, input_name in layer.inputs.items():
if input_name == output_name:
layer.inputs[key] = int_input_name
x2paddle/optimizer/optimizer.py
浏览文件 @ e2a1d944
......@@ -21,7 +21,7 @@ class GraphOptimizer(object):
self.passes = [
"interpolate_bilinear_fuse_pass", "fc_fuse_pass",
"adaptive_pool2d_fuse_pass", "batchnorm2d_fuse_pass",
"constant_fuse_pass"
"constant_fuse_pass", "reshape_fuse_pass"
]
def optimize(self, graph):
......
x2paddle/optimizer/pattern_matcher.py
浏览文件 @ e2a1d944
......@@ -63,6 +63,8 @@ class PatternMatcher(object):
pattern_layer_id_in = pattern_layer_in[i]
if pattern_layer_id_in != -1:
subgraph_ids = list(subgraph_id2layers.keys())
if layer_id_in not in subgraph_ids:
return False
if pattern_ids.index(pattern_layer_id_in) == \
subgraph_ids.index(layer_id_in):
# 判断pattern输入在pattern_ids的索引
......@@ -76,7 +78,7 @@ class PatternMatcher(object):
if not set(pattern_layer.outputs).issubset(
pattern.outputs):
# 若pattern当前layer的输出是pattern的输出，则是正确的
print("4--")
return False
else:
if len(graph.edges_out[layer_id]) != len(
......@@ -85,10 +87,12 @@ class PatternMatcher(object):
if not set(pattern_layer.outputs).issubset(
pattern.outputs):
# 若pattern当前layer的输出是pattern的输出，则是正确的
print("5--")
return False
# 当为控制流时的处理
if layer.kernel == "prim.if" or layer.kernel == "prim.loop":
if len(pattern_layer.blocks) != len(layer.blocks):
print("6--")
return False
for i, b in enumerate(pattern_layer.blocks):
match_info = get_subgraph(pattern_layer.blocks[i],
......@@ -96,13 +100,17 @@ class PatternMatcher(object):
if match_info is not False:
subgraph_id2layers.update(match_info)
else:
print("7--")
return False
pattern_index += 1
if pattern_index == len(pattern.layers):
return subgraph_id2layers
else:
return False
return subgraph_id2layers
if pattern_index == 0:
return False
if pattern_index == len(pattern.layers):
return subgraph_id2layers
return False
for i, (layer_id, layer) in enumerate(graph.layers.items()):
match_info = get_subgraph(self.pattern, graph, i)
......@@ -112,10 +120,70 @@ class PatternMatcher(object):
if len(block.layers) > 0:
self.detect_patterns_by_topo(layer.blocks[j])
def detect_patterns_by_edge(self, graph):
def detect_patterns_by_edge(self, graph, ignore_list_inputs=True):
"""当遇见顺序没有强制规定的pattern时使用该方式
"""
pass
def get_subgraph(pattern, graph, start_index):
pattern_id2layers = pattern.get_global_layers()
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]
def update(layer_id, pattern_layer_id):
layer = graph_layers[layer_id]
pattern_layer = pattern_id2layers[pattern_layer_id]
if layer.kernel != pattern_layer.kernel:
return False
subgraph_id2layers[layer_id] = layer
for i, pattern_layer_id_in in enumerate(pattern.edges_in[
pattern_layer_id]):
if pattern_layer_id_in == -1 or ignore_list_inputs:
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.edges_out.get(pattern_layer_id, 0) != 0:
if len(pattern.edges_out[pattern_layer_id]) != \
len(graph.edges_out[layer_id]):
return False
for i, pattern_layer_id_out in enumerate(pattern.edges_out[
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:
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):
out = update(layer_id, pattern_layer_id)
if out == False:
return False
else:
if len(subgraph_id2layers) == len(pattern_id2layers):
return subgraph_id2layers
else:
return False
for i, (layer_id, layer) in enumerate(graph.layers.items()):
match_info = get_subgraph(self.pattern, graph, i)
if match_info:
self.matches.append(match_info)
for j, block in enumerate(layer.blocks):
if len(block.layers) > 0:
self.detect_patterns_by_edge(layer.blocks[j])
def remove_overlapped_match(self):
""" 如果2个子图有重叠，只取前一个子图。
......
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