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X2Paddle
提交 12dc7fb1 编写于 作者: S SunAhong1993
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x2paddle/convert.py
浏览文件 @ 12dc7fb1
...@@ -209,7 +209,7 @@ def onnx2paddle(model_path, save_dir, paddle_type, params_merge=False): ...@@ -209,7 +209,7 @@ def onnx2paddle(model_path, save_dir, paddle_type, params_merge=False):
mapper.save_inference_model(save_dir, params_merge) mapper.save_inference_model(save_dir, params_merge)
def pytorch2paddle(module, save_dir, jit_type, input_examples): def pytorch2paddle(module, save_dir, jit_type, input_examples=None):
# check pytorch installation and version # check pytorch installation and version
try: try:
import torch import torch
...@@ -232,7 +232,7 @@ def pytorch2paddle(module, save_dir, jit_type, input_examples): ...@@ -232,7 +232,7 @@ def pytorch2paddle(module, save_dir, jit_type, input_examples):
if jit_type == "trace": if jit_type == "trace":
model = TraceDecoder(module, input_examples) model = TraceDecoder(module, input_examples)
else: else:
model = ScriptDecoder(module) model = ScriptDecoder(module, input_examples)
mapper = PyTorchOpMapper(model) mapper = PyTorchOpMapper(model)
mapper.paddle_graph.build() mapper.paddle_graph.build()
print("Model optimizing ...") print("Model optimizing ...")
...@@ -324,10 +324,6 @@ def main(): ...@@ -324,10 +324,6 @@ def main():
if args.params_merge: if args.params_merge:
params_merge = True params_merge = True
onnx2paddle(args.model, args.save_dir, args.paddle_type, params_merge) onnx2paddle(args.model, args.save_dir, args.paddle_type, params_merge)
elif args.framework == "pytorch":
assert args.model is not None, "--model should be defined while translating pytorch model"
pytorch2paddle(args.model, args.save_dir, args.jit_type, args.input_files)
elif args.framework == "paddle2onnx": elif args.framework == "paddle2onnx":
assert args.model is not None, "--model should be defined while translating paddle model to onnx" assert args.model is not None, "--model should be defined while translating paddle model to onnx"
paddle2onnx(args.model, args.save_dir, opset_version=args.onnx_opset) paddle2onnx(args.model, args.save_dir, opset_version=args.onnx_opset)
......
x2paddle/core/program.py
浏览文件 @ 12dc7fb1
...@@ -281,13 +281,14 @@ class PaddleGraph(object): ...@@ -281,13 +281,14 @@ class PaddleGraph(object):
else: else:
self.gen_dygraph_code(save_dir) self.gen_dygraph_code(save_dir)
self.dump_dygraph_parameter(save_dir) self.dump_dygraph_parameter(save_dir)
input_shapes = list() # 动转静
input_types = list() if len(self.inputs_info) > 0:
for input_name in self.inputs: input_shapes = list()
input_shapes.append(self.inputs_info[input_name][0]) input_types = list()
input_types.append(self.inputs_info[input_name][1]) for input_name in self.inputs:
# 如果input_files非空，则导出推理模型；其值类似[[None, 3, 224, 224]] input_shapes.append(self.inputs_info[input_name][0])
self.dygraph2static(save_dir, input_shapes, input_types) input_types.append(self.inputs_info[input_name][1])
self.dygraph2static(save_dir, input_shapes, input_types)
def gen_static_code(self, code_dir): def gen_static_code(self, code_dir):
def write_code(f, code_list, indent=0): def write_code(f, code_list, indent=0):
...@@ -424,9 +425,7 @@ class PaddleGraph(object): ...@@ -424,9 +425,7 @@ class PaddleGraph(object):
if self.edges_out.get(layer_id, 0) == 0: if self.edges_out.get(layer_id, 0) == 0:
for i, output_name in enumerate(layer.outputs): for i, output_name in enumerate(layer.outputs):
if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \ if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel):
(layer.kernel == "paddle.to_tensor" and layer.attrs["data"].startswith("params["))or \
"paddle.fluid.dygraph" in layer.kernel:
if i == 0: if i == 0:
continue continue
if output_name not in self.outputs: if output_name not in self.outputs:
...@@ -512,6 +511,8 @@ class PaddleGraph(object): ...@@ -512,6 +511,8 @@ class PaddleGraph(object):
return_code = "return {}".format(", ".join(self.outputs)) return_code = "return {}".format(", ".join(self.outputs))
self.forward_func.extend(gen_codes([return_code], indent=2)) self.forward_func.extend(gen_codes([return_code], indent=2))
for code_line in self.forward_func: for code_line in self.forward_func:
if "assert [1, 1] == 1 or [1, 1] == [1, 1], 'The [1, 1] must be [1, [1, 1]]!'" in code_line:
continue
f.write(code_line) f.write(code_line)
for code_line in self.run_func: for code_line in self.run_func:
f.write(code_line) f.write(code_line)
...@@ -593,7 +594,7 @@ class PaddleGraph(object): ...@@ -593,7 +594,7 @@ class PaddleGraph(object):
line = line.strip(", ") line = line.strip(", ")
line += ")" line += ")"
if layer.kernel == "self.create_parameter": if layer.kernel == "self.create_parameter":
self.init_func.extend(gen_codes(["self." + line], indent=indent)) self.init_func.extend(gen_codes(["self." + line], indent=2))
self.forward_func.extend(gen_codes(["{} = self.{}".format(layer.outputs[0], self.forward_func.extend(gen_codes(["{} = self.{}".format(layer.outputs[0],
layer.outputs[0])], indent=indent)) layer.outputs[0])], indent=indent))
else: else:
......
x2paddle/decoder/pytorch_decoder.py
浏览文件 @ 12dc7fb1
...@@ -41,10 +41,10 @@ class ScriptDecoder(Decoder): ...@@ -41,10 +41,10 @@ class ScriptDecoder(Decoder):
script_path (str): ScriptModule保存路径。 script_path (str): ScriptModule保存路径。
model_path (str): PyTorchModule保存路径。 model_path (str): PyTorchModule保存路径。
""" """
def __init__(self, module): def __init__(self, module, input_examples=None):
self.script = torch.jit.script(module) self.script = torch.jit.script(module)
self.graph = self._optimize_graph(self.script.inlined_graph) self.graph = self._optimize_graph(self.script.inlined_graph)
self.input_examples = None self.input_examples = input_examples
class TraceDecoder(Decoder): class TraceDecoder(Decoder):
""" PyTorchModule后使用trace方式转换为ScriptModule。 """ PyTorchModule后使用trace方式转换为ScriptModule。
...@@ -65,4 +65,5 @@ class TraceDecoder(Decoder): ...@@ -65,4 +65,5 @@ class TraceDecoder(Decoder):
exit(0) exit(0)
self.graph = self._optimize_graph(self.script.inlined_graph) self.graph = self._optimize_graph(self.script.inlined_graph)
self.input_examples = input_examples self.input_examples = input_examples
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
浏览文件 @ 12dc7fb1
...@@ -1180,7 +1180,7 @@ class OpSet9(): ...@@ -1180,7 +1180,7 @@ class OpSet9():
scale=beta) scale=beta)
add_inputs = {"x": val_mm, "y": var_beta} add_inputs = {"x": val_mm, "y": var_beta}
self.paddle_graph.add_layer( self.paddle_graph.add_layer(
"paddle.addd", "paddle.add",
inputs=add_inputs, inputs=add_inputs,
outputs=[node.layer_name]) outputs=[node.layer_name])
......
x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py
浏览文件 @ 12dc7fb1
...@@ -83,18 +83,23 @@ def aten_adaptive_avg_pool2d(mapper, graph, node): ...@@ -83,18 +83,23 @@ def aten_adaptive_avg_pool2d(mapper, graph, node):
# 处理输入1，即%_output_size.1 # 处理输入1，即%_output_size.1
if inputs_name[1] in mapper.attrs: if inputs_name[1] in mapper.attrs:
layer_attrs["output_size"] = mapper.attrs[inputs_name[1]] layer_attrs["output_size"] = mapper.attrs[inputs_name[1]]
graph.add_layer(
"paddle.nn.AdaptiveAvgPool2D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else: else:
mapper._check_input(graph, inputs_node[1], inputs_name[1], mapper._check_input(graph, inputs_node[1], inputs_name[1],
current_outputs, scope_name) current_outputs, scope_name)
layer_inputs["output_size"] = inputs_name[1] layer_inputs["output_size"] = inputs_name[1]
current_inputs.append(inputs_name[1]) current_inputs.append(inputs_name[1])
graph.add_layer(
graph.add_layer( "paddle.nn.functional.adaptive_avg_pool2d",
"paddle.nn.AdaptiveAvgPool2D", inputs=layer_inputs,
inputs=layer_inputs, outputs=layer_outputs[1:],
outputs=layer_outputs, scope_name=scope_name,
scope_name=scope_name, **layer_attrs)
**layer_attrs)
return current_inputs, current_outputs return current_inputs, current_outputs
...@@ -828,7 +833,7 @@ def aten_constant_pad_nd(mapper, graph, node): ...@@ -828,7 +833,7 @@ def aten_constant_pad_nd(mapper, graph, node):
outputs=[inputs_name[0] + "_if", output_name], outputs=[inputs_name[0] + "_if", output_name],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.sub", "prim.sub",
inputs={"y": inputs_name[0] + "_len"}, inputs={"y": inputs_name[0] + "_len"},
...@@ -859,7 +864,7 @@ def aten_constant_pad_nd(mapper, graph, node): ...@@ -859,7 +864,7 @@ def aten_constant_pad_nd(mapper, graph, node):
outputs=[output_name], outputs=[output_name],
scope_name=scope_name) scope_name=scope_name)
if_layer.add_block(block) if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
layer_inputs["input"] = inputs_name[0] layer_inputs["input"] = inputs_name[0]
block.add_layer( block.add_layer(
kernel, inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, **layer_attrs) kernel, inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, **layer_attrs)
...@@ -1186,7 +1191,7 @@ def aten_dim(mapper, graph, node): ...@@ -1186,7 +1191,7 @@ def aten_dim(mapper, graph, node):
current_outputs = [output_name] current_outputs = [output_name]
# 处理输入0，即%input.8 # 处理输入0，即%input.8
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name) mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
layer_inputs["inputs"] = inputs_name[0] layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入的list # 获取当前节点输入的list
current_inputs = list(layer_inputs.values()) current_inputs = list(layer_inputs.values())
...@@ -1468,7 +1473,7 @@ def aten_expand(mapper, graph, node): ...@@ -1468,7 +1473,7 @@ def aten_expand(mapper, graph, node):
outputs=[inputs_name[0] + "_if1", inputs_name[1] + "_var"], outputs=[inputs_name[0] + "_if1", inputs_name[1] + "_var"],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"paddle.cast", "paddle.cast",
inputs={"x": inputs_name[0]}, inputs={"x": inputs_name[0]},
...@@ -1483,7 +1488,7 @@ def aten_expand(mapper, graph, node): ...@@ -1483,7 +1488,7 @@ def aten_expand(mapper, graph, node):
dtype=string("int64"), dtype=string("int64"),
default_initializer="paddle.nn.initializer.Constant(value=0.0)") default_initializer="paddle.nn.initializer.Constant(value=0.0)")
if_layer.add_block(block) if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.type", "prim.type",
inputs={"input": inputs_name[0]}, inputs={"input": inputs_name[0]},
...@@ -1558,7 +1563,7 @@ def aten_expand_as(mapper, graph, node): ...@@ -1558,7 +1563,7 @@ def aten_expand_as(mapper, graph, node):
outputs=[inputs_name[0] + "_if1"], outputs=[inputs_name[0] + "_if1"],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.type", "prim.type",
inputs={"input": inputs_name[1]}, inputs={"input": inputs_name[1]},
...@@ -1571,7 +1576,7 @@ def aten_expand_as(mapper, graph, node): ...@@ -1571,7 +1576,7 @@ def aten_expand_as(mapper, graph, node):
scope_name=scope_name, scope_name=scope_name,
dtype=inputs_name[1] + "_type") dtype=inputs_name[1] + "_type")
if_layer.add_block(block) if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(block) if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0] if_layer.inputs["input-0"] = inputs_name[0]
if_layer.inputs["input-1"] = inputs_name[1] if_layer.inputs["input-1"] = inputs_name[1]
...@@ -1582,7 +1587,7 @@ def aten_expand_as(mapper, graph, node): ...@@ -1582,7 +1587,7 @@ def aten_expand_as(mapper, graph, node):
outputs=[inputs_name[0] + "_if2"], outputs=[inputs_name[0] + "_if2"],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"fluid.layers.cast", "fluid.layers.cast",
inputs={"x": layer_outputs[0]}, inputs={"x": layer_outputs[0]},
...@@ -2420,14 +2425,14 @@ def aten_masked_fill_(mapper, graph, node): ...@@ -2420,14 +2425,14 @@ def aten_masked_fill_(mapper, graph, node):
outputs=[inputs_name[2] + "_if"], outputs=[inputs_name[2] + "_if"],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.equal", "prim.equal",
inputs={"input": inputs_name[1] + "_mask"}, inputs={"input": inputs_name[1] + "_mask"},
outputs=[inputs_name[2] + "_1"], outputs=[inputs_name[2] + "_1"],
scope_name=scope_name) scope_name=scope_name)
if_layer.add_block(block) if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.mul", "prim.mul",
inputs={"x": inputs_name[1] + "_mask", inputs={"x": inputs_name[1] + "_mask",
...@@ -2528,14 +2533,14 @@ def aten_masked_fill(mapper, graph, node): ...@@ -2528,14 +2533,14 @@ def aten_masked_fill(mapper, graph, node):
outputs=[inputs_name[2] + "_if"], outputs=[inputs_name[2] + "_if"],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.equal", "prim.equal",
inputs={"input": inputs_name[1] + "_mask"}, inputs={"input": inputs_name[1] + "_mask"},
outputs=[inputs_name[2] + "_1"], outputs=[inputs_name[2] + "_1"],
scope_name=scope_name) scope_name=scope_name)
if_layer.add_block(block) if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.mul", "prim.mul",
inputs={"x": inputs_name[1] + "_mask", inputs={"x": inputs_name[1] + "_mask",
...@@ -4157,14 +4162,14 @@ def aten_upsample_bilinear2d(mapper, graph, node): ...@@ -4157,14 +4162,14 @@ def aten_upsample_bilinear2d(mapper, graph, node):
outputs=[inputs_name[0] + "_if1"], outputs=[inputs_name[0] + "_if1"],
scope_name=scope_name) scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]] if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
block.add_layer( block.add_layer(
"prim.var2list", "prim.var2list",
inputs={"input": inputs_name[1]}, inputs={"input": inputs_name[1]},
outputs=[inputs_name[1]], outputs=[inputs_name[1]],
scope_name=scope_name) scope_name=scope_name)
if_layer.add_block(block) if_layer.add_block(block)
block = PaddleGraph(if_layer, graph_type="dygraph") block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(block) if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[1] if_layer.inputs["input-0"] = inputs_name[1]
# 处理输入2，即%5421 # 处理输入2，即%5421
......
x2paddle/op_mapper/dygraph/pytorch2paddle/prim2code.py
浏览文件 @ 12dc7fb1
...@@ -208,9 +208,13 @@ def prim_if(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diffe ...@@ -208,9 +208,13 @@ def prim_if(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diffe
line = "if {} :".format(get_value(layer, "input", different_attrs)) line = "if {} :".format(get_value(layer, "input", different_attrs))
forward_func.extend(gen_codes([line], indent=indent)) forward_func.extend(gen_codes([line], indent=indent))
block = layer.blocks[0] block = layer.blocks[0]
b_init_lines, b_forward_lines = block.gen_dygraph_code(indent=indent + 1) if len(block.layers) == 0:
init_func.extend(b_init_lines) line = "pass"
forward_func.extend(b_forward_lines) forward_func.extend(gen_codes([line], indent=indent + 1))
else:
b_init_lines, b_forward_lines = block.gen_dygraph_code(indent=indent + 1)
init_func.extend(b_init_lines)
forward_func.extend(b_forward_lines)
block = layer.blocks[1] block = layer.blocks[1]
if len(block.layers) > 0: if len(block.layers) > 0:
b_init_lines, b_forward_lines = block.gen_dygraph_code( b_init_lines, b_forward_lines = block.gen_dygraph_code(
......
x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py
浏览文件 @ 12dc7fb1
...@@ -77,7 +77,7 @@ class PyTorchOpMapper(OpMapper): ...@@ -77,7 +77,7 @@ class PyTorchOpMapper(OpMapper):
current_node_outputs.extend(outputs) current_node_outputs.extend(outputs)
# 初始化 # 初始化
graph = PaddleGraph(parent_layer, graph_type="dygraph") graph = PaddleGraph(parent_layer=parent_layer, graph_type="dygraph")
if "TopLevelTracedModule" in str(type(self.script)): if "TopLevelTracedModule" in str(type(self.script)):
graph.set_script(self.script) graph.set_script(self.script)
current_node_outputs = [] current_node_outputs = []
...@@ -277,7 +277,7 @@ class PyTorchOpMapper(OpMapper): ...@@ -277,7 +277,7 @@ class PyTorchOpMapper(OpMapper):
control_output_id = index - 1 control_output_id = index - 1
output_node_name = parent_layer.outputs[control_output_id] output_node_name = parent_layer.outputs[control_output_id]
current_outputs = [output_node_name] current_outputs = [output_node_name]
self._check_input(graph, node, input_node_name, current_outputs) self._check_input(graph, node, input_node_name, current_outputs, scope_name)
graph.add_layer( graph.add_layer(
"prim.equal", "prim.equal",
inputs={'input': input_node_name}, inputs={'input': input_node_name},
......
x2paddle/optimizer/fusion/dygraph/adaptive_pool2d_fuser.py
浏览文件 @ 12dc7fb1
...@@ -13,6 +13,7 @@ ...@@ -13,6 +13,7 @@
# limitations under the License. # limitations under the License.
import numpy as np import numpy as np
import copy
from x2paddle.optimizer.pattern_matcher import FuseBase from x2paddle.optimizer.pattern_matcher import FuseBase
from x2paddle.core.program import PaddleGraph, PaddleLayer from x2paddle.core.program import PaddleGraph, PaddleLayer
from x2paddle.core.util import * from x2paddle.core.util import *
...@@ -21,11 +22,13 @@ from x2paddle.core.util import * ...@@ -21,11 +22,13 @@ from x2paddle.core.util import *
class DygraphAdaptivePool2dFuser(FuseBase): class DygraphAdaptivePool2dFuser(FuseBase):
def __init__(self): def __init__(self):
super(DygraphAdaptivePool2dFuser, self).__init__(graph_type="dygraph") super(DygraphAdaptivePool2dFuser, self).__init__(graph_type="dygraph")
self.patterns = list()
def build_pattern(self): def build_pattern(self):
""" 描述需要替换的adaptive pool2d图结构。 """ 描述需要替换的adaptive pool2d图结构。
adaptive pool2d层模式python实现代码示例: adaptive pool2d层模式python实现代码示例:
x68 = fluid.layers.shape(input=x60) 模式一：
x68 = prim.shape(input=x60)
x69 = len(x68) x69 = len(x68)
x70 = x69 <= 2 x70 = x69 <= 2
if x70 : if x70 :
...@@ -38,57 +41,64 @@ class DygraphAdaptivePool2dFuser(FuseBase): ...@@ -38,57 +41,64 @@ class DygraphAdaptivePool2dFuser(FuseBase):
for _x79 in range(x77): for _x79 in range(x77):
x80 = [6, 6][_x79] x80 = [6, 6][_x79]
x73.append(x80) x73.append(x80)
x81 = fluid.layers.adaptive_pool2d(input=x60, pool_size=x73, pool_type='avg') x81 = paddle.nn.functional.adaptive_avg_pool2d(input=x60, pool_size=x73, pool_type='avg')
模式二：
x64 = x60.shape
x65 = len(x64)
x66 = x65 > 2
if x66 :
pass
else:
raise RaiseException('AssertionError: ')
x69 = self.pool2d3(x60)
""" """
def gen_name(id): def gen_name(id):
return "x" + str(id) return "x" + str(id)
self.pattern.add_layer( # 模式一：
"fluid.layers.shape", pattern = PaddleGraph(graph_type="dygraph")
pattern.add_layer(
"prim.shape",
inputs={'input': "pool-input-0"}, inputs={'input': "pool-input-0"},
outputs=[gen_name(1)]) outputs=[gen_name(1)])
self.pattern.add_layer( pattern.add_layer(
"prim.len", inputs={"input": gen_name(1)}, outputs=[gen_name(6)]) "prim.len", inputs={"input": gen_name(1)}, outputs=[gen_name(6)])
self.pattern.add_layer( pattern.add_layer(
"prim.le", inputs={"x": gen_name(6)}, outputs=[gen_name(8)], y=2) "prim.le", inputs={"x": gen_name(6)}, outputs=[gen_name(8)], y=2)
self.pattern.add_layer("prim.if", {'input': gen_name(8)}, [gen_name(9)]) pattern.add_layer("prim.if", {'input': gen_name(8)}, [gen_name(9)])
if_layer = self.pattern.layers[list(self.pattern.layers.keys())[-1]] if_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(if_layer, graph_type="dygraph") pattern_block0 = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
pattern_block0.add_layer( pattern_block0.add_layer(
"prim.exception", "prim.exception",
inputs={}, inputs={},
outputs=[gen_name(9)], outputs=[gen_name(9)],
input="Exception") input="Exception")
if_layer.add_block(pattern_block0) if_layer.add_block(pattern_block0)
pattern_block1 = PaddleGraph(if_layer, graph_type="dygraph") pattern_block1 = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(pattern_block1) if_layer.add_block(pattern_block1)
self.pattern.add_layer("prim.list", inputs={}, outputs=[gen_name(10)]) pattern.add_layer("prim.list", inputs={}, outputs=[gen_name(10)])
self.pattern.add_layer( pattern.add_layer(
"prim.slice", "prim.slice",
inputs={"input": gen_name(1), }, inputs={"input": gen_name(1), },
outputs=[gen_name(12)], outputs=[gen_name(12)])
start=-1, pattern.add_layer(
end=100,
step=1)
self.pattern.add_layer(
"prim.len", inputs={"input": gen_name(12)}, outputs=[gen_name(14)]) "prim.len", inputs={"input": gen_name(12)}, outputs=[gen_name(14)])
self.pattern.add_layer( pattern.add_layer(
"prim.list", "prim.list",
inputs={"input1": gen_name(14)}, inputs={"input1": gen_name(14)},
outputs=[gen_name(15)], outputs=[gen_name(15)])
input0=2) pattern.add_layer(
self.pattern.add_layer(
"prim.min", inputs={"input": gen_name(15)}, outputs=[gen_name(16)]) "prim.min", inputs={"input": gen_name(15)}, outputs=[gen_name(16)])
self.pattern.add_layer("prim.loop", {'input': gen_name(16)}, pattern.add_layer("prim.loop", {'input': gen_name(16)},
[gen_name(17), gen_name(18)]) [gen_name(17), gen_name(18)])
loop_layer = self.pattern.layers[list(self.pattern.layers.keys())[-1]] loop_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block = PaddleGraph(loop_layer, graph_type="dygraph") pattern_block = PaddleGraph(loop_layer, graph_type="dygraph")
pattern_block.add_layer( pattern_block.add_layer(
"prim.getitem", "prim.getitem",
inputs={"index": gen_name(18)}, inputs={"index": gen_name(18)},
outputs=[gen_name(19)], outputs=[gen_name(19)])
list=[6, 6])
pattern_block.add_layer( pattern_block.add_layer(
"prim.append", "prim.append",
inputs={"list": gen_name(10), inputs={"list": gen_name(10),
...@@ -97,14 +107,45 @@ class DygraphAdaptivePool2dFuser(FuseBase): ...@@ -97,14 +107,45 @@ class DygraphAdaptivePool2dFuser(FuseBase):
loop_layer.inputs["input-0"] = gen_name(10) loop_layer.inputs["input-0"] = gen_name(10)
loop_layer.add_block(pattern_block) loop_layer.add_block(pattern_block)
pool_attrs = {'pool_type': string("avg")} pool_attrs = {'pool_type': string("avg")}
self.pattern.add_layer( pattern.add_layer(
"fluid.layers.adaptive_pool2d", "paddle.nn.functional.adaptive_avg_pool2d",
inputs={'input': "pool-input-0", inputs={'input': "pool-input-0",
"pool_size": gen_name(10)}, "pool_size": gen_name(10)},
outputs=[gen_name(21)], outputs=[gen_name(21)],
**pool_attrs) **pool_attrs)
self.pattern.build(inputs={"input-0": "pool-input-0", }) pattern.build(inputs={"input-0": "pool-input-0", })
self.patterns.append(pattern)
# 模式二：
pattern = PaddleGraph(graph_type="dygraph")
pattern.add_layer(
"prim.shape",
inputs={'input': "pool-input-0"},
outputs=[gen_name(0)])
pattern.add_layer(
"prim.len", inputs={"input": gen_name(0)}, outputs=[gen_name(1)])
pattern.add_layer(
"prim.gt", inputs={"x": gen_name(1)}, outputs=[gen_name(2)], y=2)
pattern.add_layer("prim.if", {'input': gen_name(2)}, [gen_name(3)])
if_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(pattern_block0)
pattern_block1 = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
pattern_block1.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(4)],
input="Exception")
if_layer.add_block(pattern_block1)
pattern.add_layer(
"paddle.nn.AdaptiveAvgPool2D",
inputs={"input": "pool-input-0"},
outputs=["pool1", gen_name(5)])
pattern.build(inputs={"input-0": "pool-input-0",
"input-1": "pool-input-0",})
self.patterns.append(pattern)
def insert_new_layer(self, graph, parameters, matches): def insert_new_layer(self, graph, parameters, matches):
parameters = graph.parameters parameters = graph.parameters
new_layer = self.gen_new_layer(parameters, matches) new_layer = self.gen_new_layer(parameters, matches)
...@@ -114,20 +155,21 @@ class DygraphAdaptivePool2dFuser(FuseBase): ...@@ -114,20 +155,21 @@ class DygraphAdaptivePool2dFuser(FuseBase):
def gen_new_layer(self, parameters, matches): def gen_new_layer(self, parameters, matches):
layers_id = list(matches.keys()) layers_id = list(matches.keys())
layer = matches[layers_id[11]] if matches[layers_id[-1]].kernel == "paddle.nn.functional.adaptive_avg_pool2d":
pool_size = layer.attrs["list"] layer = matches[layers_id[11]]
layer = matches[layers_id[0]] pool_size = layer.attrs["list"]
input_name = layer.inputs["input"] layer = matches[layers_id[0]]
layer = matches[layers_id[-1]] input_name = layer.inputs["input"]
output_name = layer.outputs[0] layer = matches[layers_id[-1]]
pool_type = layer.attrs["pool_type"] output_name = layer.outputs[0]
attrs = dict() attrs = dict()
attrs["pool_size"] = pool_size attrs["output_size"] = pool_size
attrs["pool_type"] = pool_type new_layer = PaddleLayer(
new_layer = PaddleLayer( layers_id[0],
layers_id[0], "paddle.nn.functional.adaptive_avg_pool2d",
"fluid.layers.adaptive_pool2d", inputs={"input": input_name},
inputs={"input": input_name}, outputs=[output_name],
outputs=[output_name], **attrs)
**attrs) else:
new_layer = copy.deepcopy(matches[layers_id[-1]])
return new_layer return new_layer
x2paddle/optimizer/fusion/dygraph/batchnorm2d_fuser.py
浏览文件 @ 12dc7fb1
...@@ -25,33 +25,33 @@ class DygraphBatchNorm2dFuser(FuseBase): ...@@ -25,33 +25,33 @@ class DygraphBatchNorm2dFuser(FuseBase):
def build_pattern(self): def build_pattern(self):
""" 描述需要替换的batchnorm2d图结构。 """ 描述需要替换的batchnorm2d图结构。
batchnorm2d层模式python实现代码示例: batchnorm2d层模式python实现代码示例:
x336 = fluid.layers.shape(input=x334) x2011 = x2009.shape
x336 = len(x336) x2011 = len(x2011)
x337 = x336 != 4 x2012 = x2011 != 4
if x337 : if x2012 :
raise RaiseException('Exception') raise RaiseException('Exception')
if False : if False :
x351 = fluid.layers.shape(input=x334) x2026 = x2009.shape
x352 = x351[0] x2027 = x2026[0]
x353 = len(x351) x2028 = len(x2026)
x354 = x353 - 2 x2029 = x2028 - 2
x357 = x352 x2032 = x2027
for _x356 in range(x354): for _x2031 in range(x2029):
x358 = _x356 + 2 x2033 = _x2031 + 2
x359 = x351[x358] x2034 = x2026[x2033]
x360 = x357 * x359 x2035 = x2032 * x2034
x355 = x360 x2030 = x2035
x361 = x355 == 1 x2036 = x2030 == 1
if x361 : if x2036 :
raise RaiseException('Exception') raise RaiseException('Exception')
x364 = self.batchnorm7(x334) x2039 = self.batchnorm50(x2009)
""" """
def gen_name(id): def gen_name(id):
return "x" + str(id) return "x" + str(id)
self.pattern.add_layer( self.pattern.add_layer(
"fluid.layers.shape", "prim.shape",
inputs={'input': "bn-input-0"}, inputs={'input': "bn-input-0"},
outputs=[gen_name(0)]) outputs=[gen_name(0)])
self.pattern.add_layer( self.pattern.add_layer(
...@@ -60,20 +60,20 @@ class DygraphBatchNorm2dFuser(FuseBase): ...@@ -60,20 +60,20 @@ class DygraphBatchNorm2dFuser(FuseBase):
"prim.ne", inputs={"x": gen_name(0)}, outputs=[gen_name(1)], y=4) "prim.ne", inputs={"x": gen_name(0)}, outputs=[gen_name(1)], y=4)
self.pattern.add_layer("prim.if", {'input': gen_name(1)}, [gen_name(2)]) self.pattern.add_layer("prim.if", {'input': gen_name(1)}, [gen_name(2)])
if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]] if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block0 = PaddleGraph(parent_layer=if_layer1, graph_type="dygraph")
pattern_block0.add_layer( pattern_block0.add_layer(
"prim.exception", "prim.exception",
inputs={}, inputs={},
outputs=[gen_name(3)], outputs=[gen_name(3)],
input="Exception") input="Exception")
if_layer1.add_block(pattern_block0) if_layer1.add_block(pattern_block0)
pattern_block1 = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block1 = PaddleGraph(parent_layer=if_layer1, graph_type="dygraph")
if_layer1.add_block(pattern_block1) if_layer1.add_block(pattern_block1)
self.pattern.add_layer("prim.if", {}, [gen_name(4)], input=False) self.pattern.add_layer("prim.if", {}, [gen_name(4)], input=False)
if_layer2 = self.pattern.layers[list(self.pattern.layers.keys())[-1]] if_layer2 = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(if_layer2, graph_type="dygraph") pattern_block0 = PaddleGraph(parent_layer=if_layer2, graph_type="dygraph")
pattern_block0.add_layer( pattern_block0.add_layer(
"fluid.layers.shape", "prim.shape",
inputs={'input': "bn-input-0"}, inputs={'input': "bn-input-0"},
outputs=[gen_name(5)]) outputs=[gen_name(5)])
pattern_block0.add_layer( pattern_block0.add_layer(
...@@ -93,7 +93,7 @@ class DygraphBatchNorm2dFuser(FuseBase): ...@@ -93,7 +93,7 @@ class DygraphBatchNorm2dFuser(FuseBase):
outputs=[gen_name(8.1), gen_name(10)]) outputs=[gen_name(8.1), gen_name(10)])
loop_layer = pattern_block0.layers[list(pattern_block0.layers.keys())[ loop_layer = pattern_block0.layers[list(pattern_block0.layers.keys())[
-1]] -1]]
pattern_block0_block0 = PaddleGraph(loop_layer, graph_type="dygraph") pattern_block0_block0 = PaddleGraph(parent_layer=loop_layer, graph_type="dygraph")
pattern_block0_block0.add_layer( pattern_block0_block0.add_layer(
"prim.add", inputs={"x": gen_name(10)}, outputs=[gen_name(11)], y=2) "prim.add", inputs={"x": gen_name(10)}, outputs=[gen_name(11)], y=2)
pattern_block0_block0.add_layer( pattern_block0_block0.add_layer(
...@@ -119,27 +119,24 @@ class DygraphBatchNorm2dFuser(FuseBase): ...@@ -119,27 +119,24 @@ class DygraphBatchNorm2dFuser(FuseBase):
"prim.if", inputs={"input": gen_name(14)}, outputs=[gen_name(15)]) "prim.if", inputs={"input": gen_name(14)}, outputs=[gen_name(15)])
if_layer21 = pattern_block0.layers[list(pattern_block0.layers.keys())[ if_layer21 = pattern_block0.layers[list(pattern_block0.layers.keys())[
-1]] -1]]
pattern_block0_block0 = PaddleGraph(if_layer21, graph_type="dygraph") pattern_block0_block0 = PaddleGraph(parent_layer=if_layer21, graph_type="dygraph")
pattern_block0_block0.add_layer( pattern_block0_block0.add_layer(
"prim.exception", "prim.exception",
inputs={}, inputs={},
outputs=[gen_name(15)], outputs=[gen_name(15)],
input="Exception") input="Exception")
if_layer21.add_block(pattern_block0_block0) if_layer21.add_block(pattern_block0_block0)
pattern_block0_block1 = PaddleGraph(if_layer21, graph_type="dygraph") pattern_block0_block1 = PaddleGraph(parent_layer=if_layer21, graph_type="dygraph")
if_layer21.add_block(pattern_block0_block1) if_layer21.add_block(pattern_block0_block1)
if_layer2.add_block(pattern_block0) if_layer2.add_block(pattern_block0)
pattern_block1 = PaddleGraph(if_layer2, graph_type="dygraph") pattern_block1 = PaddleGraph(parent_layer=if_layer2, graph_type="dygraph")
if_layer2.add_block(pattern_block1) if_layer2.add_block(pattern_block1)
if_layer2.inputs["input-0"] = "bn-input-0" if_layer2.inputs["input-0"] = "bn-input-0"
self.pattern.add_layer( self.pattern.add_layer(
"paddle.nn.BatchNorm", "paddle.nn.BatchNorm",
inputs={"input": "bn-input-0"}, inputs={"input": "bn-input-0"},
outputs=[gen_name(16), gen_name(17)], outputs=[gen_name(16), gen_name(17)],
is_test=True, is_test=True)
num_channels=160,
momentum=0.1,
epsilon=0.001)
self.pattern.build(inputs={"input-0": "bn-input-0"}) self.pattern.build(inputs={"input-0": "bn-input-0"})
def insert_new_layer(self, graph, parameters, matches): def insert_new_layer(self, graph, parameters, matches):
...@@ -148,9 +145,6 @@ class DygraphBatchNorm2dFuser(FuseBase): ...@@ -148,9 +145,6 @@ class DygraphBatchNorm2dFuser(FuseBase):
graph.layers[new_layer_id] = new_layer graph.layers[new_layer_id] = new_layer
matches.pop(new_layer_id) matches.pop(new_layer_id)
# for layer in matches.values():
# print(layer.outputs)
# print("-------")
def gen_new_layer(self, parameters, matches): def gen_new_layer(self, parameters, matches):
layers_id = list(matches.keys()) layers_id = list(matches.keys())
......
x2paddle/optimizer/fusion/dygraph/fc_fuser.py
浏览文件 @ 12dc7fb1
...@@ -31,14 +31,14 @@ class DygraphFcFuser(FuseBase): ...@@ -31,14 +31,14 @@ class DygraphFcFuser(FuseBase):
x134 = x133 == 2 x134 = x133 == 2
if x134 : if x134 :
classifier_6_weight = self.classifier_6_weight classifier_6_weight = self.classifier_6_weight
x136 = fluid.layers.transpose(x=classifier_6_weight, perm=[1, 0]) x136 = paddle.transpose(x=classifier_6_weight, perm=[1, 0])
classifier_6_bias = self.classifier_6_bias classifier_6_bias = self.classifier_6_bias
x137 = paddle.addmm(input=classifier_6_bias, x=x128, y=x136, beta=1, alpha=1) x137 = paddle.addmm(input=classifier_6_bias, x=x128, y=x136, beta=1, alpha=1)
x135 = x137 x135 = x137
else: else:
classifier_6_weight = self.classifier_6_weight classifier_6_weight = self.classifier_6_weight
x138 = fluid.layers.transpose(x=classifier_6_weight, perm=[1, 0]) x138 = paddle.transpose(x=classifier_6_weight, perm=[1, 0])
x139 = fluid.layers.matmul(x=x128, y=x138) x139 = paddle.matmul(x=x128, y=x138)
classifier_6_bias = self.classifier_6_bias classifier_6_bias = self.classifier_6_bias
x140 = x139 + 1 * classifier_6_bias x140 = x139 + 1 * classifier_6_bias
x135 = x140 x135 = x140
...@@ -48,7 +48,7 @@ class DygraphFcFuser(FuseBase): ...@@ -48,7 +48,7 @@ class DygraphFcFuser(FuseBase):
return "x" + str(id) return "x" + str(id)
self.pattern.add_layer( self.pattern.add_layer(
"fluid.layers.shape", "prim.shape",
inputs={'input': "fc-input-0"}, inputs={'input': "fc-input-0"},
outputs=[gen_name(2)]) outputs=[gen_name(2)])
self.pattern.add_layer( self.pattern.add_layer(
...@@ -61,22 +61,20 @@ class DygraphFcFuser(FuseBase): ...@@ -61,22 +61,20 @@ class DygraphFcFuser(FuseBase):
self.pattern.add_layer("prim.if", {'input': gen_name(3)}, [gen_name(4)]) self.pattern.add_layer("prim.if", {'input': gen_name(3)}, [gen_name(4)])
self.pattern.outputs.append(gen_name(4)) self.pattern.outputs.append(gen_name(4))
if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]] if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block0 = PaddleGraph(parent_layer=if_layer1, graph_type="dygraph")
pattern_block0.add_layer( pattern_block0.add_layer(
"fluid.dygraph.base.to_variable", "self.create_parameter",
inputs={}, inputs={},
outputs=[gen_name(5)], outputs=[gen_name(5)])
value="params[{}]".format(string(gen_name(5))))
pattern_block0.add_layer( pattern_block0.add_layer(
"fluid.layers.transpose", "paddle.transpose",
inputs={"x": gen_name(5)}, inputs={"x": gen_name(5)},
outputs=[gen_name(6)], outputs=[gen_name(6)],
perm=[1, 0]) perm=[1, 0])
pattern_block0.add_layer( pattern_block0.add_layer(
"fluid.dygraph.base.to_variable", "self.create_parameter",
inputs={}, inputs={},
outputs=[gen_name(7)], outputs=[gen_name(7)])
value="params[{}]".format(string(gen_name(7))))
pattern_block0.add_layer( pattern_block0.add_layer(
"paddle.addmm", "paddle.addmm",
inputs={"input": gen_name(7), inputs={"input": gen_name(7),
...@@ -90,14 +88,13 @@ class DygraphFcFuser(FuseBase): ...@@ -90,14 +88,13 @@ class DygraphFcFuser(FuseBase):
pattern_block0.add_layer( pattern_block0.add_layer(
"prim.equal", inputs={'input': gen_name(8)}, outputs=[gen_name(4)]) "prim.equal", inputs={'input': gen_name(8)}, outputs=[gen_name(4)])
if_layer1.add_block(pattern_block0) if_layer1.add_block(pattern_block0)
pattern_block1 = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block1 = PaddleGraph(parent_layer=if_layer1, graph_type="dygraph")
pattern_block1.add_layer( pattern_block1.add_layer(
"fluid.dygraph.base.to_variable", "self.create_parameter",
inputs={}, inputs={},
outputs=[gen_name(5)], outputs=[gen_name(5)])
value="params[{}]".format(string(gen_name(5))))
pattern_block1.add_layer( pattern_block1.add_layer(
"fluid.layers.transpose", "paddle.transpose",
inputs={"x": gen_name(5)}, inputs={"x": gen_name(5)},
outputs=[gen_name(6)], outputs=[gen_name(6)],
perm=[1, 0]) perm=[1, 0])
...@@ -108,10 +105,9 @@ class DygraphFcFuser(FuseBase): ...@@ -108,10 +105,9 @@ class DygraphFcFuser(FuseBase):
outputs=[gen_name(9)]) outputs=[gen_name(9)])
if_layer1.inputs["input-1"] = "fc-input-0" if_layer1.inputs["input-1"] = "fc-input-0"
pattern_block1.add_layer( pattern_block1.add_layer(
"fluid.dygraph.base.to_variable", "self.create_parameter",
inputs={}, inputs={},
outputs=[gen_name(12)], outputs=[gen_name(12)])
value="params[{}]".format(string(gen_name(12))))
pattern_block1.add_layer( pattern_block1.add_layer(
"prim.add_", "prim.add_",
inputs={"x": gen_name(9), inputs={"x": gen_name(9),
...@@ -137,9 +133,9 @@ class DygraphFcFuser(FuseBase): ...@@ -137,9 +133,9 @@ class DygraphFcFuser(FuseBase):
layer = matches[layers_id[3]] layer = matches[layers_id[3]]
output_name = layer.outputs[0] output_name = layer.outputs[0]
layer = matches[layers_id[4]] layer = matches[layers_id[4]]
weight_name = layer.attrs["value"][8:-2] weight_name = layer.outputs[0]
layer = matches[layers_id[6]] layer = matches[layers_id[6]]
bias_name = layer.attrs["value"][8:-2] bias_name = layer.outputs[0]
attrs = dict() attrs = dict()
attrs["in_features"] = parameters[weight_name].shape[1] attrs["in_features"] = parameters[weight_name].shape[1]
attrs["out_features"] = parameters[weight_name].shape[0] attrs["out_features"] = parameters[weight_name].shape[0]
......
x2paddle/optimizer/fusion/dygraph/interpolate_bilinear_fuser.py
浏览文件 @ 12dc7fb1
...@@ -12,6 +12,7 @@ ...@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import copy
import numpy as np import numpy as np
from x2paddle.optimizer.pattern_matcher import FuseBase from x2paddle.optimizer.pattern_matcher import FuseBase
from x2paddle.core.program import PaddleGraph, PaddleLayer from x2paddle.core.program import PaddleGraph, PaddleLayer
...@@ -32,135 +33,40 @@ class DygraphInterpolateBilinearFuser(FuseBase): ...@@ -32,135 +33,40 @@ class DygraphInterpolateBilinearFuser(FuseBase):
def build_pattern(self): def build_pattern(self):
""" 描述需要替换的双线性插值图结构。 """ 描述需要替换的双线性插值图结构。
interpolate_bilinear层模式python实现代码示例: interpolate_bilinear层模式python实现代码示例:
x3016 = fluid.layers.shape(input=x3005) x2195 = x2181.shape
x3016 = len(x3016) x2195 = len(x2195)
x3017 = x3016 - 2 x2196 = x2195 - 2
x3018 = [] x2197 = []
for _x3020 in range(x3017): for _x2199 in range(x2196):
x3018.append(None) x2197.append(None)
x3021 = (x3005, x8, None, None) x2200 = (x2181, x8, None, None)
x3022 = fluid.layers.shape(input=x3005) ...
x3022 = len(x3022) x2267 = x2266 == 3
x3023 = x3022 == 3 if x2267 :
if x3023 :
raise RaiseException('Exception') raise RaiseException('Exception')
x3024 = None x2268 = None
else: else:
x3026 = fluid.layers.shape(input=x3005) x2270 = x2181.shape
x3026 = len(x3026) x2270 = len(x2270)
x3027 = x3026 == 4 x2271 = x2270 == 4
if x3027 : if x2271 :
x3044, x3045, x3046, x3047 = x3021 x2274 = x2197[0]
x3048 = x3045 is None x2275 = x2197[1]
if x3048 : x2233_isinstance = isinstance(x2233, paddle.fluid.Variable)
x3051 = x3046 is None if x2233_isinstance :
x3049 = x3051 x2233 = x2233.numpy().tolist()
x3050 = x3045 x2276 = paddle.nn.functional.interpolate(x=x2181, size=x2233, scale_factor=x2274, align_corners=False, align_mode=0, mode='bilinear')
else: x2272 = x2276
x3052 = x3045
x3049 = False
x3050 = x3052
if x3049 :
raise RaiseException('Exception')
x3055 = x3050 is not None
if x3055 :
x3058 = x3050
x3059 = x3046 is not None
x3056 = x3059
x3057 = x3058
else:
x3056 = False
x3057 = x3050
if x3056 :
raise RaiseException('Exception')
x3060 = None
x3061 = None
else:
x3060 = x3046
x3061 = x3057
x3063 = x3060 is not None
if x3063 :
x3065 = x3060
x3066 = len(x3065)
x3067 = x3066 != 2
if x3067 :
raise RaiseException('Exception')
x3064 = x3065
else:
x3064 = x3060
x3070 = x3061 is not None
if x3070 :
x3072 = x3061
x3071 = x3072
else:
x3071 = None
if x3070 :
x3073 = x3071
else:
x3074 = x3064 is not None
if x3074 :
x3076 = x3064
x3075 = x3076
else:
raise RaiseException('Exception')
x3075 = None
x3078 = x3047 is None
if x3078 :
x3080 = len(x3075)
x3081 = x3080 > 0
x3086 = 0
for x3083 in range(2147483647):
x3087 = x3075[x3086]
x3088 = math.floor(x3087)
x3089 = x3088 != x3087
if x3089 :
x3090 = False
x3091 = x3089
else:
x3090 = None
x3091 = None
if x3089 :
x3092 = x3090
x3093 = x3091
else:
x3092 = True
x3093 = x3089
x3094 = x3086 + 1
x3095 = x3094 < x3080
x3096 = x3095 and x3092
x3082 = x3093
x3083 = x3094
if x3082 :
import warnings
warnings.warn('The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. ', stacklevel=2)
x3099 = []
for _x3101 in range(2):
x3102 = _x3101 + 2
x3103 = fluid.layers.shape(x3044)[x3102]
x3104 = float(x3103)
x3105 = x3075[_x3101]
x3106 = x3104 * x3105
x3107 = math.floor(x3106)
x3099.append(x3107)
x3073 = x3099
x3108 = x3018[0]
x3109 = x3018[1]
x3073_isinstance = isinstance(x3073, paddle.fluid.Variable)
if x3073_isinstance :
x3073 = x3073.numpy().tolist()
assert x3108 == x3109, 'The x3108 must be x3109!'
x3110 = paddle.nn.functional.interpolate(x=x3005, size=x3073, scale_factor=x3108, align_corners=False, align_mode=0)
x3028 = x3110
else: else:
x3111 = fluid.layers.shape(input=x3005) x2277 = x2181.shape
x3111 = len(x3111) x2277 = len(x2277)
x3112 = x3111 == 5 x2278 = x2277 == 5
if x3112 : if x2278 :
raise RaiseException('Exception') raise RaiseException('Exception')
else: else:
raise RaiseException('Exception') raise RaiseException('Exception')
x3028 = None x2272 = None
x3024 = x3028 x2268 = x2272
""" """
def gen_name(id): def gen_name(id):
...@@ -168,7 +74,7 @@ class DygraphInterpolateBilinearFuser(FuseBase): ...@@ -168,7 +74,7 @@ class DygraphInterpolateBilinearFuser(FuseBase):
if self.version_gt_150: if self.version_gt_150:
self.pattern.add_layer( self.pattern.add_layer(
"fluid.layers.shape", "prim.shape",
inputs={"input": "interpolate-input-0"}, inputs={"input": "interpolate-input-0"},
outputs=[gen_name(9)]) outputs=[gen_name(9)])
self.pattern.add_layer( self.pattern.add_layer(
...@@ -200,686 +106,36 @@ class DygraphInterpolateBilinearFuser(FuseBase): ...@@ -200,686 +106,36 @@ class DygraphInterpolateBilinearFuser(FuseBase):
"prim.tuple", "prim.tuple",
inputs={ inputs={
"input0": "interpolate-input-0", "input0": "interpolate-input-0",
"input1": "interpolate-input-1", "input1": "interpolate-input-4",
}, },
outputs=[gen_name(13)], outputs=[gen_name(12)],
input2=None, input2=None,
input3=None) input3=None)
self.pattern.add_layer(
"fluid.layers.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(14)])
self.pattern.add_layer(
"prim.len",
inputs={"input": gen_name(14)},
outputs=[gen_name(14)])
self.pattern.add_layer( self.pattern.add_layer(
"prim.eq", "prim.eq",
inputs={"x": gen_name(14)}, inputs={"x": "interpolate-input-2"},
outputs=[gen_name(15)], outputs=[gen_name(10.1)],
y=3) y=3)
self.pattern.add_layer( self.pattern.add_layer(
"prim.if", "prim.if",
inputs={"input": gen_name(15)}, inputs={"input": gen_name(10.1)},
outputs=[gen_name(16)])
if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[
-1]]
pattern_block = PaddleGraph(if_layer1, graph_type="dygraph")
pattern_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(17)],
input="Exception")
pattern_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(16)], input=None)
if_layer1.add_block(pattern_block)
pattern_block = PaddleGraph(if_layer1, graph_type="dygraph")
pattern_block.add_layer(
"fluid.layers.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(18)])
pattern_block.add_layer(
"prim.len",
inputs={"input": gen_name(18)},
outputs=[gen_name(18)])
pattern_block.add_layer(
"prim.eq",
inputs={"x": gen_name(18)},
outputs=[gen_name(19)],
y=4)
pattern_block.add_layer(
"prim.if",
inputs={"input": gen_name(19)},
outputs=[gen_name(20)])
if_layer2 = pattern_block.layers[list(pattern_block.layers.keys())[
-1]]
pattern_block_block = PaddleGraph(if_layer2, graph_type="dygraph")
pattern_block_block.add_layer(
"prim.tuple_unpack",
inputs={"input": gen_name(13)},
outputs=[
gen_name(34), gen_name(35), gen_name(36), gen_name(37)
])
pattern_block_block.add_layer(
"prim.is",
inputs={"x": gen_name(35)},
outputs=[gen_name(38)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(38)},
outputs=[gen_name(39), gen_name(40)])
if_layer3 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer3, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.is",
inputs={"x": gen_name(36)},
outputs=[gen_name(41)],
y=None)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(41)},
outputs=[gen_name(39)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(35)},
outputs=[gen_name(40)])
if_layer3.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer3, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(35)},
outputs=[gen_name(42)])
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(39)], input=False)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(35)},
outputs=[gen_name(40)])
if_layer3.add_block(pattern_block_block_block)
if_layer3.inputs.update({
"input-0": gen_name(36),
'input-1': gen_name(35),
'input-2': gen_name(35),
})
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(39)},
outputs=[gen_name(43)])
if_layer4 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer4, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(44)],
input="Exception")
if_layer4.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer4, graph_type="dygraph")
if_layer4.add_block(pattern_block_block_block)
pattern_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(40)},
outputs=[gen_name(45)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(45)},
outputs=[gen_name(46), gen_name(47)])
if_layer5 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer5, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(40)},
outputs=[gen_name(48)])
pattern_block_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(36)},
outputs=[gen_name(49)],
y=None)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(49)},
outputs=[gen_name(46)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(48)},
outputs=[gen_name(47)])
if_layer5.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer5, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(46)], input=False)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(40)},
outputs=[gen_name(47)])
if_layer5.add_block(pattern_block_block_block)
if_layer5.inputs.update({
"input-0": gen_name(40),
"input-1": gen_name(36),
"input-3": gen_name(40)
})
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(46)},
outputs=[gen_name(50), gen_name(51)])
if_layer6 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer6, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(52)],
input="Exception")
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(50)], input=None)
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(51)], input=None)
if_layer6.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer6, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(36)},
outputs=[gen_name(50)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(47)},
outputs=[gen_name(51)])
if_layer6.add_block(pattern_block_block_block)
if_layer6.inputs.update({
"input-0": gen_name(36),
"input-1": gen_name(47)
})
pattern_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(50)},
outputs=[gen_name(53)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(53)},
outputs=[gen_name(54)])
if_layer7 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer7, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(50)},
outputs=[gen_name(55)])
pattern_block_block_block.add_layer(
"prim.len",
inputs={"input": gen_name(55)},
outputs=[gen_name(56)])
pattern_block_block_block.add_layer(
"prim.ne",
inputs={"x": gen_name(56)},
outputs=[gen_name(57)],
y=2)
pattern_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(57)},
outputs=[gen_name(58)])
if_layer8 = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block = PaddleGraph(
if_layer8, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(59)],
input="Exception")
if_layer8.add_block(pattern_block_block_block_block)
pattern_block_block_block_block = PaddleGraph(
if_layer8, graph_type="dygraph")
if_layer8.add_block(pattern_block_block_block_block)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(55)},
outputs=[gen_name(54)])
if_layer7.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer7, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(50)},
outputs=[gen_name(54)])
if_layer7.add_block(pattern_block_block_block)
if_layer7.inputs.update({
"input-0": gen_name(50),
"input-1": gen_name(50)
})
pattern_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(51)},
outputs=[gen_name(60)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(60)},
outputs=[gen_name(61)])
if_layer9 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer9, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(51)},
outputs=[gen_name(62)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(62)},
outputs=[gen_name(61)])
if_layer9.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer9, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(54)},
outputs=[gen_name(64)],
y=None)
pattern_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(64)},
outputs=[gen_name(65)])
if_layer11 = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block = PaddleGraph(
if_layer11, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(54)},
outputs=[gen_name(66)])
pattern_block_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(66)},
outputs=[gen_name(65)])
if_layer11.add_block(pattern_block_block_block_block)
pattern_block_block_block_block = PaddleGraph(
if_layer11, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(67)],
input="Exception")
pattern_block_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(65)], input=None)
if_layer11.add_block(pattern_block_block_block_block)
if_layer11.inputs.update({"input-0": gen_name(54), })
pattern_block_block_block.add_layer(
"prim.is",
inputs={"x": gen_name(37)},
outputs=[gen_name(68)],
y=None)
pattern_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(68)},
outputs=[gen_name(69)])
if_layer12 = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block = PaddleGraph(
if_layer12, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.len",
inputs={"input": gen_name(65)},
outputs=[gen_name(70)])
pattern_block_block_block_block.add_layer(
"prim.gt",
inputs={"x": gen_name(70)},
outputs=[gen_name(71)],
y=0)
pattern_block_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(72)], input=0)
pattern_block_block_block_block.add_layer(
"prim.loop",
inputs={},
outputs=[gen_name(74), gen_name(75), gen_name(76.1)],
input=2147483647)
loop_layer = pattern_block_block_block_block.layers[list(
pattern_block_block_block_block.layers.keys())[-1]]
pattern_loop_block = PaddleGraph(loop_layer, graph_type="dygraph")
pattern_loop_block.add_layer(
"prim.getitem",
inputs={"list": gen_name(65),
"element": gen_name(72)},
outputs=[gen_name(74.1)])
pattern_loop_block.add_layer(
"prim.floor",
inputs={"input": gen_name(74.1)},
outputs=[gen_name(75.1)])
pattern_loop_block.add_layer(
"prim.ne",
inputs={"x": gen_name(75.1),
"y": gen_name(74.1)},
outputs=[gen_name(76)])
pattern_loop_block.add_layer(
"prim.if",
inputs={"input": gen_name(76)},
outputs=[gen_name(77)])
if_layer13 = pattern_loop_block.layers[list(
pattern_loop_block.layers.keys())[-1]]
pattern_loop_block_block = PaddleGraph(
if_layer13, graph_type="dygraph")
pattern_loop_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(77)], input=False)
if_layer13.add_block(pattern_loop_block_block)
pattern_loop_block_block = PaddleGraph(
if_layer13, graph_type="dygraph")
pattern_loop_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(77)], input=True)
if_layer13.add_block(pattern_loop_block_block)
pattern_loop_block.add_layer(
"prim.add",
inputs={"x": gen_name(72)},
outputs=[gen_name(81)],
y=1)
pattern_loop_block.add_layer(
"prim.lt",
inputs={"x": gen_name(81),
"y": gen_name(70)},
outputs=[gen_name(82)])
pattern_loop_block.add_layer(
"prim.and",
inputs={"x": gen_name(82),
"y": gen_name(77)},
outputs=[gen_name(83)])
pattern_loop_block.add_layer(
"prim.equal",
inputs={"input": gen_name(76)},
outputs=[gen_name(74)])
pattern_loop_block.add_layer(
"prim.equal",
inputs={"input": gen_name(81)},
outputs=[gen_name(75)])
loop_layer.add_block(pattern_loop_block)
loop_layer.inputs.update({
"input-0": gen_name(65),
"input-1": gen_name(72),
"input-2": gen_name(72),
"input-3": gen_name(70)
})
pattern_block_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(74)},
outputs=[gen_name(84)])
if_layer15 = pattern_block_block_block_block.layers[list(
pattern_block_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block_block = PaddleGraph(
if_layer15, graph_type="dygraph")
pattern_block_block_block_block_block.add_layer(
"prim.warnings",
inputs={},
outputs=[gen_name(85)],
stacklevel=2,
input="...")
if_layer15.add_block(pattern_block_block_block_block_block)
pattern_block_block_block_block_block = PaddleGraph(
if_layer15, graph_type="dygraph")
if_layer15.add_block(pattern_block_block_block_block_block)
if_layer12.add_block(pattern_block_block_block_block)
pattern_block_block_block_block = PaddleGraph(
if_layer12, graph_type="dygraph")
if_layer12.add_block(pattern_block_block_block_block)
if_layer12.inputs.update({
"input-0": gen_name(65),
"input-1": gen_name(65),
})
pattern_block_block_block.add_layer(
"prim.list", inputs={}, outputs=[gen_name(86)])
pattern_block_block_block.add_layer(
"prim.loop",
inputs={},
outputs=[gen_name(87), gen_name(88)],
input=2)
loop_layer = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_loop_block = PaddleGraph(loop_layer, graph_type="dygraph")
pattern_loop_block.add_layer(
"prim.add",
inputs={"x": gen_name(88)},
outputs=[gen_name(89)],
y=2)
pattern_loop_block.add_layer(
"prim.shape_dim",
inputs={"input": gen_name(34),
"dim": gen_name(89)},
outputs=[gen_name(90)])
pattern_loop_block.add_layer(
"prim.float",
inputs={"input": gen_name(90)},
outputs=[gen_name(91)])
pattern_loop_block.add_layer(
"prim.getitem",
inputs={"list": gen_name(65),
"element": gen_name(88)},
outputs=[gen_name(92)])
pattern_loop_block.add_layer(
"prim.mul",
inputs={"x": gen_name(91),
"y": gen_name(92)},
outputs=[gen_name(93)])
pattern_loop_block.add_layer(
"prim.floor",
inputs={"input": gen_name(93)},
outputs=[gen_name(94)])
pattern_loop_block.add_layer(
"prim.append",
inputs={"list": gen_name(86),
"element": gen_name(94)},
outputs=[])
loop_layer.add_block(pattern_loop_block)
loop_layer.inputs.update({
"input-0": gen_name(34),
"input-1": gen_name(65),
"input-2": gen_name(86)
})
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(86)},
outputs=[gen_name(61)])
if_layer9.add_block(pattern_block_block_block)
if_layer9.inputs.update({
"input-0": gen_name(51),
"input-1": gen_name(54),
"input-2": gen_name(54),
"input-3": gen_name(37),
"input-4": gen_name(34)
})
pattern_block_block.add_layer(
"prim.getitem",
inputs={"list": gen_name(11)},
outputs=[gen_name(95)],
element=0)
pattern_block_block.add_layer(
"prim.getitem",
inputs={"list": gen_name(11)},
outputs=[gen_name(96)],
element=1)
pattern_block_block.add_layer(
"prim.isinstance",
inputs={"input": gen_name(61)},
outputs=["interpolate-input-0_isinstance"],
cls="paddle.fluid.Variable")
pattern_block_block.add_layer(
"prim.if", {"input": "interpolate-input-0_isinstance"},
outputs=["interpolate-input-0_if1"])
if_layer_isinstance = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer_isinstance, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.var2list",
inputs={"input": gen_name(61)},
outputs=[gen_name(61)])
if_layer_isinstance.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer_isinstance, graph_type="dygraph")
if_layer_isinstance.add_block(pattern_block_block_block)
if_layer_isinstance.inputs["input-0"] = gen_name(61)
pattern_block_block.add_layer(
"prim.assert",
inputs={"key": gen_name(95),
"value": gen_name(96)},
outputs=[gen_name(97) + "_assert"],
type="eq")
pattern_block_block.add_layer(
"paddle.nn.functional.interpolate",
inputs={
"input": "interpolate-input-0",
"size": gen_name(61),
"scale_factor": gen_name(95)
},
outputs=[gen_name(97)],
align_corners=False,
align_mode=0)
pattern_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(97)},
outputs=[gen_name(20)])
if_layer2.add_block(pattern_block_block)
pattern_block_block = PaddleGraph(if_layer2, graph_type="dygraph")
pattern_block_block.add_layer(
"fluid.layers.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(98)])
pattern_block_block.add_layer(
"prim.len",
inputs={"input": gen_name(98)},
outputs=[gen_name(98)])
pattern_block_block.add_layer(
"prim.eq",
inputs={"x": gen_name(98)},
outputs=[gen_name(99)],
y=5)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(99)},
outputs=[gen_name(100)])
if_layer16 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer16, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(101)],
input="Exception")
if_layer16.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer16, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(102)],
input="Exception")
if_layer16.add_block(pattern_block_block_block)
pattern_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(20)], input=None)
if_layer2.add_block(pattern_block_block)
if_layer2.inputs.update({
"input-0": gen_name(13),
"input-1": gen_name(13),
"input-2": "interpolate-input-0",
"input-3": gen_name(11),
"input-5": gen_name(11),
})
pattern_block.add_layer(
"prim.equal",
inputs={"input": gen_name(20)},
outputs=[gen_name(16)])
if_layer1.add_block(pattern_block)
if_layer1.inputs.update({
"input-2": "interpolate-input-0",
"input-4": gen_name(13),
"input-7": gen_name(11),
"input-9": gen_name(11),
"input-11": "interpolate-input-0",
"input-12": "interpolate-input-0",
})
self.pattern.build(inputs={
"input-0": "interpolate-input-0",
"input-1": "interpolate-input-1"
})
else:
self.pattern.add_layer(
"fluid.layers.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(9)])
self.pattern.add_layer(
"prim.len",
inputs={"input": gen_name(9)},
outputs=[gen_name(9)])
self.pattern.add_layer(
"prim.sub",
inputs={"x": gen_name(9)},
outputs=[gen_name(10)],
y=2)
self.pattern.add_layer(
"prim.list", inputs={}, outputs=[gen_name(11)])
self.pattern.add_layer(
"prim.loop",
inputs={"input": gen_name(10)},
outputs=[gen_name(12.1), gen_name(12.2)])
loop_layer = self.pattern.layers[list(self.pattern.layers.keys())[
-1]]
pattern_block = PaddleGraph(loop_layer, graph_type="dygraph")
pattern_block.add_layer(
"prim.append",
inputs={"list": gen_name(11)},
outputs=[],
element=None)
loop_layer.inputs["input-0"] = gen_name(11)
loop_layer.add_block(pattern_block)
self.pattern.add_layer(
"prim.tuple",
inputs={
"input0": "interpolate-input-0",
"input1": "interpolate-input-1",
},
outputs=[gen_name(13)],
input2=None,
input3=None)
self.pattern.add_layer(
"fluid.layers.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(14)])
self.pattern.add_layer(
"prim.len",
inputs={"input": gen_name(14)},
outputs=[gen_name(14)]) outputs=[gen_name(14)])
self.pattern.add_layer(
"prim.eq",
inputs={"x": gen_name(14)},
outputs=[gen_name(15)],
y=3)
self.pattern.add_layer(
"prim.if",
inputs={"input": gen_name(15)},
outputs=[gen_name(16)])
if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[ if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[
-1]] -1]]
pattern_block = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block = PaddleGraph(parent_layer=if_layer1, graph_type="dygraph")
pattern_block.add_layer( pattern_block.add_layer(
"prim.exception", "prim.exception",
inputs={}, inputs={},
outputs=[gen_name(17)], outputs=[gen_name(15)],
input="Exception") input="Exception")
pattern_block.add_layer( pattern_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(16)], input=None) "prim.equal", inputs={}, outputs=[gen_name(14)], input=None)
if_layer1.add_block(pattern_block) if_layer1.add_block(pattern_block)
pattern_block = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block = PaddleGraph(parent_layer=if_layer1, graph_type="dygraph")
pattern_block.add_layer( pattern_block.add_layer(
"fluid.layers.shape", "prim.shape",
inputs={"input": "interpolate-input-0"}, inputs={"input": "interpolate-input-0"},
outputs=[gen_name(18)]) outputs=[gen_name(18)])
pattern_block.add_layer( pattern_block.add_layer(
...@@ -891,524 +147,28 @@ class DygraphInterpolateBilinearFuser(FuseBase): ...@@ -891,524 +147,28 @@ class DygraphInterpolateBilinearFuser(FuseBase):
inputs={"x": gen_name(18)}, inputs={"x": gen_name(18)},
outputs=[gen_name(19)], outputs=[gen_name(19)],
y=4) y=4)
pattern_block.add_layer( pattern_block.add_layer(
"prim.if", "prim.if",
inputs={"input": gen_name(19)}, inputs={"input": gen_name(19)},
outputs=[gen_name(20)]) outputs=[gen_name(20)])
if_layer2 = pattern_block.layers[list(pattern_block.layers.keys())[ if_layer2 = pattern_block.layers[list(pattern_block.layers.keys())[
-1]] -1]]
pattern_block_block = PaddleGraph(if_layer2, graph_type="dygraph") pattern_block_block = PaddleGraph(parent_layer=if_layer2, graph_type="dygraph")
pattern_block_block.add_layer(
"prim.tuple_unpack",
inputs={"input": gen_name(13)},
outputs=[
gen_name(34), gen_name(35), gen_name(36), gen_name(37)
])
pattern_block_block.add_layer(
"prim.is",
inputs={"x": gen_name(35)},
outputs=[gen_name(38)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(38)},
outputs=[gen_name(39), gen_name(40)])
if_layer3 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer3, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.is",
inputs={"x": gen_name(36)},
outputs=[gen_name(41)],
y=None)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(41)},
outputs=[gen_name(39)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(35)},
outputs=[gen_name(40)])
if_layer3.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer3, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(35)},
outputs=[gen_name(42)])
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(39)], input=False)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(35)},
outputs=[gen_name(40)])
if_layer3.add_block(pattern_block_block_block)
if_layer3.inputs.update({
"input-0": gen_name(36),
'input-1': gen_name(35),
'input-2': gen_name(35),
})
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(39)},
outputs=[gen_name(43)])
if_layer4 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer4, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(44)],
input="Exception")
if_layer4.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer4, graph_type="dygraph")
if_layer4.add_block(pattern_block_block_block)
pattern_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(40)},
outputs=[gen_name(45)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(45)},
outputs=[gen_name(46), gen_name(47)])
if_layer5 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer5, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(40)},
outputs=[gen_name(48)])
pattern_block_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(36)},
outputs=[gen_name(49)],
y=None)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(49)},
outputs=[gen_name(46)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(48)},
outputs=[gen_name(47)])
if_layer5.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer5, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(46)], input=False)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(40)},
outputs=[gen_name(47)])
if_layer5.add_block(pattern_block_block_block)
if_layer5.inputs.update({
"input-0": gen_name(40),
"input-1": gen_name(36),
"input-3": gen_name(40)
})
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(46)},
outputs=[gen_name(50), gen_name(51)])
if_layer6 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer6, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(52)],
input="Exception")
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(50)], input=None)
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(51)], input=None)
if_layer6.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer6, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(36)},
outputs=[gen_name(50)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(47)},
outputs=[gen_name(51)])
if_layer6.add_block(pattern_block_block_block)
if_layer6.inputs.update({
"input-0": gen_name(36),
"input-1": gen_name(47)
})
pattern_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(50)},
outputs=[gen_name(53)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(53)},
outputs=[gen_name(54)])
if_layer7 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer7, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(50)},
outputs=[gen_name(55)])
pattern_block_block_block.add_layer(
"prim.len",
inputs={"input": gen_name(55)},
outputs=[gen_name(56)])
pattern_block_block_block.add_layer(
"prim.ne",
inputs={"x": gen_name(56)},
outputs=[gen_name(57)],
y=2)
pattern_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(57)},
outputs=[gen_name(58)])
if_layer8 = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block = PaddleGraph(
if_layer8, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(59)],
input="Exception")
if_layer8.add_block(pattern_block_block_block_block)
pattern_block_block_block_block = PaddleGraph(
if_layer8, graph_type="dygraph")
if_layer8.add_block(pattern_block_block_block_block)
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(55)},
outputs=[gen_name(54)])
if_layer7.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer7, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(50)},
outputs=[gen_name(54)])
if_layer7.add_block(pattern_block_block_block)
if_layer7.inputs.update({
"input-0": gen_name(50),
"input-1": gen_name(50)
})
pattern_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(51)},
outputs=[gen_name(60)],
y=None)
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(60)},
outputs=[gen_name(61)])
if_layer9 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer9, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(51)},
outputs=[gen_name(62)])
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(62)},
outputs=[gen_name(61)])
if_layer9.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer9, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(61)], input=None)
if_layer9.add_block(pattern_block_block_block)
if_layer9.inputs.update({"input-0": gen_name(51)})
pattern_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(60)},
outputs=[gen_name(63)])
if_layer10 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(
if_layer10, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(61)},
outputs=[gen_name(63)])
if_layer10.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(
if_layer10, graph_type="dygraph")
pattern_block_block_block.add_layer(
"prim.isnot",
inputs={"x": gen_name(54)},
outputs=[gen_name(64)],
y=None)
pattern_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(64)},
outputs=[gen_name(65)])
if_layer11 = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block = PaddleGraph(
if_layer11, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(54)},
outputs=[gen_name(66)])
pattern_block_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(66)},
outputs=[gen_name(65)])
if_layer11.add_block(pattern_block_block_block_block)
pattern_block_block_block_block = PaddleGraph(
if_layer11, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.exception",
inputs={},
outputs=[gen_name(67)],
input="Exception")
pattern_block_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(65)], input=None)
if_layer11.add_block(pattern_block_block_block_block)
if_layer11.inputs.update({"input-0": gen_name(54), })
pattern_block_block_block.add_layer(
"prim.is",
inputs={"x": gen_name(37)},
outputs=[gen_name(68)],
y=None)
pattern_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(68)},
outputs=[gen_name(69)])
if_layer12 = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block = PaddleGraph(
if_layer12, graph_type="dygraph")
pattern_block_block_block_block.add_layer(
"prim.len",
inputs={"input": gen_name(65)},
outputs=[gen_name(70)])
pattern_block_block_block_block.add_layer(
"prim.gt",
inputs={"x": gen_name(70)},
outputs=[gen_name(71)],
y=0)
pattern_block_block_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(72)], input=0)
pattern_block_block_block_block.add_layer(
"prim.loop",
inputs={},
outputs=[gen_name(74), gen_name(75), gen_name(76.1)],
input=2147483647)
loop_layer = pattern_block_block_block_block.layers[list(
pattern_block_block_block_block.layers.keys())[-1]]
pattern_loop_block = PaddleGraph(loop_layer, graph_type="dygraph")
pattern_loop_block.add_layer(
"prim.getitem",
inputs={"list": gen_name(65),
"element": gen_name(72)},
outputs=[gen_name(74.1)])
pattern_loop_block.add_layer(
"prim.floor",
inputs={"input": gen_name(74.1)},
outputs=[gen_name(75.1)])
pattern_loop_block.add_layer(
"prim.ne",
inputs={"x": gen_name(75.1),
"y": gen_name(74.1)},
outputs=[gen_name(76)])
pattern_loop_block.add_layer(
"prim.if",
inputs={"input": gen_name(76)},
outputs=[gen_name(77), gen_name(78)])
if_layer13 = pattern_loop_block.layers[list(
pattern_loop_block.layers.keys())[-1]]
pattern_loop_block_block = PaddleGraph(
if_layer13, graph_type="dygraph")
pattern_loop_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(77)], input=False)
pattern_loop_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(76)},
outputs=[gen_name(78)])
if_layer13.add_block(pattern_loop_block_block)
pattern_loop_block_block = PaddleGraph(
if_layer13, graph_type="dygraph")
pattern_loop_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(77)], input=None)
pattern_loop_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(78)], input=None)
if_layer13.add_block(pattern_loop_block_block)
if_layer13.inputs.update({"input-0": gen_name(76), })
pattern_loop_block.add_layer(
"prim.if",
inputs={"input": gen_name(76)},
outputs=[gen_name(79), gen_name(80)])
if_layer14 = pattern_loop_block.layers[list(
pattern_loop_block.layers.keys())[-1]]
pattern_loop_block_block = PaddleGraph(
if_layer14, graph_type="dygraph")
pattern_loop_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(77)},
outputs=[gen_name(79)])
pattern_loop_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(78)},
outputs=[gen_name(80)])
if_layer14.add_block(pattern_loop_block_block)
pattern_loop_block_block = PaddleGraph(
if_layer14, graph_type="dygraph")
pattern_loop_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(79)], input=True)
pattern_loop_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(76)},
outputs=[gen_name(80)])
if_layer14.add_block(pattern_loop_block_block)
if_layer14.inputs.update({
"input-0": gen_name(77),
"input-1": gen_name(78),
"input-2": gen_name(76)
})
pattern_loop_block.add_layer(
"prim.add",
inputs={"x": gen_name(72)},
outputs=[gen_name(81)],
y=1)
pattern_loop_block.add_layer(
"prim.lt",
inputs={"x": gen_name(81),
"y": gen_name(70)},
outputs=[gen_name(82)])
pattern_loop_block.add_layer(
"prim.and",
inputs={"x": gen_name(82),
"y": gen_name(79)},
outputs=[gen_name(83)])
pattern_loop_block.add_layer(
"prim.equal",
inputs={"input": gen_name(80)},
outputs=[gen_name(74)])
pattern_loop_block.add_layer(
"prim.equal",
inputs={"input": gen_name(81)},
outputs=[gen_name(75)])
loop_layer.add_block(pattern_loop_block)
loop_layer.inputs.update({
"input-0": gen_name(65),
"input-1": gen_name(72),
"input-2": gen_name(72),
"input-3": gen_name(70)
})
pattern_block_block_block_block.add_layer(
"prim.if",
inputs={"input": gen_name(74)},
outputs=[gen_name(84)])
if_layer15 = pattern_block_block_block_block.layers[list(
pattern_block_block_block_block.layers.keys())[-1]]
pattern_block_block_block_block_block = PaddleGraph(
if_layer15, graph_type="dygraph")
pattern_block_block_block_block_block.add_layer(
"prim.warnings",
inputs={},
outputs=[gen_name(85)],
stacklevel=2,
input="...")
if_layer15.add_block(pattern_block_block_block_block_block)
pattern_block_block_block_block_block = PaddleGraph(
if_layer15, graph_type="dygraph")
if_layer15.add_block(pattern_block_block_block_block_block)
if_layer12.add_block(pattern_block_block_block_block)
pattern_block_block_block_block = PaddleGraph(
if_layer12, graph_type="dygraph")
if_layer12.add_block(pattern_block_block_block_block)
if_layer12.inputs.update({
"input-0": gen_name(65),
"input-1": gen_name(65),
})
pattern_block_block_block.add_layer(
"prim.list", inputs={}, outputs=[gen_name(86)])
pattern_block_block_block.add_layer(
"prim.loop",
inputs={},
outputs=[gen_name(87), gen_name(88)],
input=2)
loop_layer = pattern_block_block_block.layers[list(
pattern_block_block_block.layers.keys())[-1]]
pattern_loop_block = PaddleGraph(loop_layer, graph_type="dygraph")
pattern_loop_block.add_layer(
"prim.add",
inputs={"x": gen_name(88)},
outputs=[gen_name(89)],
y=2)
pattern_loop_block.add_layer(
"prim.shape_dim",
inputs={"input": gen_name(34),
"dim": gen_name(89)},
outputs=[gen_name(90)])
pattern_loop_block.add_layer(
"prim.float",
inputs={"input": gen_name(90)},
outputs=[gen_name(91)])
pattern_loop_block.add_layer(
"prim.getitem",
inputs={"list": gen_name(65),
"element": gen_name(88)},
outputs=[gen_name(92)])
pattern_loop_block.add_layer(
"prim.mul",
inputs={"x": gen_name(91),
"y": gen_name(92)},
outputs=[gen_name(93)])
pattern_loop_block.add_layer(
"prim.floor",
inputs={"input": gen_name(93)},
outputs=[gen_name(94)])
pattern_loop_block.add_layer(
"prim.append",
inputs={"list": gen_name(86),
"element": gen_name(94)},
outputs=[])
loop_layer.add_block(pattern_loop_block)
loop_layer.inputs.update({
"input-0": gen_name(34),
"input-1": gen_name(65),
"input-2": gen_name(86)
})
pattern_block_block_block.add_layer(
"prim.equal",
inputs={"input": gen_name(86)},
outputs=[gen_name(63)])
if_layer10.add_block(pattern_block_block_block)
if_layer10.inputs.update({
"input-0": gen_name(61),
"input-1": gen_name(54),
"input-2": gen_name(54),
"input-3": gen_name(37),
"input-4": gen_name(34)
})
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.getitem", "prim.getitem",
inputs={"list": gen_name(11)}, inputs={"list": gen_name(11)},
outputs=[gen_name(95)], outputs=[gen_name(21)],
element=0) element=0)
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.getitem", "prim.getitem",
inputs={"list": gen_name(11)}, inputs={"list": gen_name(11)},
outputs=[gen_name(96)], outputs=[gen_name(22)],
element=1) element=1)
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.isinstance", "prim.isinstance",
inputs={"input": gen_name(63)}, inputs={"input": "interpolate-input-3"},
outputs=["interpolate-input-0_isinstance"], outputs=["interpolate-input-0_isinstance"],
cls="paddle.fluid.Variable") cls="paddle.fluid.Variable")
pattern_block_block.add_layer( pattern_block_block.add_layer(
...@@ -1420,133 +180,127 @@ class DygraphInterpolateBilinearFuser(FuseBase): ...@@ -1420,133 +180,127 @@ class DygraphInterpolateBilinearFuser(FuseBase):
if_layer_isinstance, graph_type="dygraph") if_layer_isinstance, graph_type="dygraph")
pattern_block_block_block.add_layer( pattern_block_block_block.add_layer(
"prim.var2list", "prim.var2list",
inputs={"input": gen_name(63)}, inputs={"input": "interpolate-input-3"},
outputs=[gen_name(63)]) outputs=["interpolate-input-3"])
if_layer_isinstance.add_block(pattern_block_block_block) if_layer_isinstance.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph( pattern_block_block_block = PaddleGraph(
if_layer_isinstance, graph_type="dygraph") if_layer_isinstance, graph_type="dygraph")
if_layer_isinstance.add_block(pattern_block_block_block) if_layer_isinstance.add_block(pattern_block_block_block)
if_layer_isinstance.inputs["input-0"] = gen_name(63) if_layer_isinstance.inputs["input-0"] = "interpolate-input-3"
pattern_block_block.add_layer(
"prim.assert",
inputs={"key": gen_name(95),
"value": gen_name(96)},
outputs=[gen_name(97) + "_assert"],
type="eq")
pattern_block_block.add_layer( pattern_block_block.add_layer(
"paddle.nn.functional.interpolate", "paddle.nn.functional.interpolate",
inputs={ inputs={
"input": "interpolate-input-0", "input": "interpolate-input-0",
"size": gen_name(63), "size": "interpolate-input-3",
"scale_factor": gen_name(95) "scale_factor": gen_name(21)
}, },
outputs=[gen_name(97)], outputs=[gen_name(23)])
align_corners=False,
align_mode=0)
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.equal", "prim.equal",
inputs={"input": gen_name(97)}, inputs={"input": gen_name(23)},
outputs=[gen_name(20)]) outputs=[gen_name(20)])
if_layer2.add_block(pattern_block_block) if_layer2.add_block(pattern_block_block)
pattern_block_block = PaddleGraph(if_layer2, graph_type="dygraph") pattern_block_block = PaddleGraph(if_layer2, graph_type="dygraph")
pattern_block_block.add_layer( pattern_block_block.add_layer(
"fluid.layers.shape", "prim.shape",
inputs={"input": "interpolate-input-0"}, inputs={"input": "interpolate-input-0"},
outputs=[gen_name(98)]) outputs=[gen_name(24)])
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.len", "prim.len",
inputs={"input": gen_name(98)}, inputs={"input": gen_name(24)},
outputs=[gen_name(98)]) outputs=[gen_name(24)])
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.eq", "prim.eq",
inputs={"x": gen_name(98)}, inputs={"x": gen_name(24)},
outputs=[gen_name(99)], outputs=[gen_name(25)],
y=5) y=5)
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.if", "prim.if",
inputs={"input": gen_name(99)}, inputs={"input": gen_name(25)},
outputs=[gen_name(100)]) outputs=[gen_name(26)])
if_layer16 = pattern_block_block.layers[list( if_layer3 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]] pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph( pattern_block_block_block = PaddleGraph(
if_layer16, graph_type="dygraph") parent_layer=if_layer3, graph_type="dygraph")
pattern_block_block_block.add_layer( pattern_block_block_block.add_layer(
"prim.exception", "prim.exception",
inputs={}, inputs={},
outputs=[gen_name(101)], outputs=[gen_name(27)],
input="Exception") input="Exception")
if_layer16.add_block(pattern_block_block_block) if_layer3.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph( pattern_block_block_block = PaddleGraph(
if_layer16, graph_type="dygraph") parent_layer=if_layer3, graph_type="dygraph")
pattern_block_block_block.add_layer( pattern_block_block_block.add_layer(
"prim.exception", "prim.exception",
inputs={}, inputs={},
outputs=[gen_name(102)], outputs=[gen_name(28)],
input="Exception") input="Exception")
if_layer16.add_block(pattern_block_block_block) if_layer3.add_block(pattern_block_block_block)
pattern_block_block.add_layer( pattern_block_block.add_layer(
"prim.equal", inputs={}, outputs=[gen_name(20)], input=None) "prim.equal", inputs={}, outputs=[gen_name(20)], input=None)
if_layer2.add_block(pattern_block_block) if_layer2.add_block(pattern_block_block)
if_layer2.inputs.update({ if_layer2.inputs.update({
"input-0": gen_name(13), "input-0": "interpolate-input-0",
"input-1": gen_name(13), "input-1": "interpolate-input-3",
"input-2": "interpolate-input-0", "input-2": "interpolate-input-3",
"input-3": gen_name(11), "input-3": gen_name(11),
"input-5": gen_name(11), "input-5": gen_name(11),
}) })
pattern_block.add_layer( pattern_block.add_layer(
"prim.equal", "prim.equal",
inputs={"input": gen_name(20)}, inputs={"input": gen_name(20)},
outputs=[gen_name(16)]) outputs=[gen_name(14)])
if_layer1.add_block(pattern_block) if_layer1.add_block(pattern_block)
if_layer1.inputs.update({ if_layer1.inputs.update({
"input-2": "interpolate-input-0", 'input-2': 'interpolate-input-0',
"input-4": gen_name(13), 'input-4': gen_name(11),
"input-7": gen_name(11), 'input-6': gen_name(11),
"input-9": gen_name(11), 'input-8': 'interpolate-input-0',
"input-11": "interpolate-input-0", 'input-9': 'interpolate-input-3',
"input-12": "interpolate-input-0", 'input-10': 'interpolate-input-0'
}) })
self.pattern.build(inputs={ self.pattern.build(inputs={
"input-0": "interpolate-input-0", "input-0": "interpolate-input-0",
"input-1": "interpolate-input-1" "input-1": "interpolate-input-1",
"input-2": "interpolate-input-2",
"input-3": "interpolate-input-3",
"input-4": "interpolate-input-4"
}) })
def insert_new_layer(self, graph, parameters, matches): def insert_new_layer(self, graph, parameters, matches):
new_layers = self.gen_new_layer(parameters, matches) new_layer = self.gen_new_layer(parameters, matches)
new_layer_id = list(matches.keys())[0] global_layers = graph.get_global_layers()
graph.layers[new_layer_id] = new_layers[0] new_matches = dict()
matches.pop(new_layer_id) is_match = False
new_layer_id = list(matches.keys())[0] for layer_id, layer in global_layers.items():
graph.layers[new_layer_id] = new_layers[1] if layer_id == list(matches.keys())[0] and not is_match:
block_layer = new_layers[1].blocks[0].layers.pop( new_matches[layer_id] = layer
list(new_layers[1].blocks[0].layers.keys())[-1]) is_match = True
new_layers[1].blocks[0].layers[new_layer_id + ".0.0"] = block_layer if is_match:
matches.pop(new_layer_id) new_matches[layer_id] = layer
new_layer_id = list(matches.keys())[0] if layer_id == list(matches.keys())[-1]:
graph.layers[new_layer_id] = new_layers[2] break
matches.pop(new_layer_id) new_layer_id = new_layer.layer_id
graph.layers[new_layer_id] = new_layer
new_matches.pop(new_layer_id)
matches.clear()
for layer_id, layer in new_matches.items():
matches[layer_id] = layer
def gen_new_layer(self, parameters, matches): def gen_new_layer(self, parameters, matches):
layers = list() layers = list()
layers_id = list(matches.keys()) layers_id = list(matches.keys())
layer = matches[layers_id[6]] layer = matches[layers_id[6]]
size = layer.inputs["input1"] size = layer.inputs["input1"]
layer = matches[layers_id[92]] layer = matches[layers_id[19]]
layer.inputs["input"] = size new_layer = copy.deepcopy(layer)
layers.append(layer) layer = matches[layers_id[9]]
layer = matches[layers_id[93]] new_layer.outputs[0] = layer.outputs[0]
block_layer = layer.blocks[0].layers[list(layer.blocks[0].layers.keys()) new_layer.layer_id = layers_id[7]
[0]] new_layer.inputs.pop("scale_factor")
block_layer.inputs["input"] = size new_layer.inputs["size"] = size
block_layer.outputs[0] = size return new_layer
layer.inputs["input-0"] = size
layers.append(layer)
layer = matches[layers_id[-1]]
outputs = layer.outputs
layer = matches[layers_id[96]]
layer.inputs.pop("scale_factor")
layer.inputs["size"] = size
layer.outputs = outputs
layers.append(layer)
return layers
x2paddle/optimizer/optimizer.py
浏览文件 @ 12dc7fb1
...@@ -24,9 +24,12 @@ class GraphOptimizer(object): ...@@ -24,9 +24,12 @@ class GraphOptimizer(object):
self.passes = ["trace_fc_fuse_pass"] self.passes = ["trace_fc_fuse_pass"]
else: else:
self.passes = [ self.passes = [
"dygraph_constant_fuse_pass", "dygraph_batchnorm2d_fuse_pass", "dygraph_constant_fuse_pass",
"dygraph_interpolate_bilinear_fuse_pass", "dygraph_fc_fuse_pass", "dygraph_batchnorm2d_fuse_pass",
"dygraph_adaptive_pool2d_fuse_pass", "dygraph_reshape_fuse_pass", "dygraph_interpolate_bilinear_fuse_pass",
"dygraph_fc_fuse_pass",
"dygraph_adaptive_pool2d_fuse_pass",
"dygraph_reshape_fuse_pass",
"dygraph_dropout_fuse_pass" "dygraph_dropout_fuse_pass"
] ]
elif source_frame == "caffe": elif source_frame == "caffe":
......
x2paddle/optimizer/pattern_matcher.py
浏览文件 @ 12dc7fb1
...@@ -85,7 +85,11 @@ class PatternMatcher(object): ...@@ -85,7 +85,11 @@ class PatternMatcher(object):
# 判断subgraph中的节点是否被外部图使用到(如若被使用到则无效) # 判断subgraph中的节点是否被外部图使用到(如若被使用到则无效)
if layer_id in graph.edges_out: if layer_id in graph.edges_out:
if pattern_layer_id not in pattern.edges_out: if pattern_layer_id not in pattern.edges_out:
if not set(pattern_layer.outputs).issubset( if "paddle.nn" in layer.kernel and "functional" not in layer.kernel:
pattern_layer_opt = pattern_layer.outputs[1:]
else:
pattern_layer_opt = pattern_layer.outputs
if not set(pattern_layer_opt).issubset(
pattern.outputs): pattern.outputs):
# 若pattern当前layer的输出是pattern的输出，则是正确的 # 若pattern当前layer的输出是pattern的输出，则是正确的
if pattern_index == 0 or is_subblock: if pattern_index == 0 or is_subblock:
...@@ -97,7 +101,11 @@ class PatternMatcher(object): ...@@ -97,7 +101,11 @@ class PatternMatcher(object):
if len(graph.edges_out[layer_id]) != len( if len(graph.edges_out[layer_id]) != len(
pattern.edges_out[pattern_layer_id]): pattern.edges_out[pattern_layer_id]):
# 如果在每个节点edges_in相同的情况下，edges_out数目相同则说明无节点在subgraph外被用到 # 如果在每个节点edges_in相同的情况下，edges_out数目相同则说明无节点在subgraph外被用到
if not set(pattern_layer.outputs).issubset( if "paddle.nn" in layer.kernel and "functional" not in layer.kernel:
pattern_layer_opt = pattern_layer.outputs[1:]
else:
pattern_layer_opt = pattern_layer.outputs
if not set(pattern_layer_opt).issubset(
pattern.outputs): pattern.outputs):
# 若pattern当前layer的输出是pattern的输出，则是正确的 # 若pattern当前layer的输出是pattern的输出，则是正确的
if pattern_index == 0 or is_subblock: if pattern_index == 0 or is_subblock:
...@@ -105,6 +113,7 @@ class PatternMatcher(object): ...@@ -105,6 +113,7 @@ class PatternMatcher(object):
else: else:
subgraph_id2layers.pop(layer_id) subgraph_id2layers.pop(layer_id)
continue continue
# 当为控制流时的处理 # 当为控制流时的处理
if layer.kernel == "prim.if" or layer.kernel == "prim.loop": if layer.kernel == "prim.if" or layer.kernel == "prim.loop":
if len(pattern_layer.blocks) != len(layer.blocks): if len(pattern_layer.blocks) != len(layer.blocks):
......
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