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提交 bf803137 编写于 作者: S SunAhong1993
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remove tf optimizer

上级 ee48679c
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x2paddle/optimizer/tensorflow/batch_norm.py 已删除 100644 → 0
浏览文件 @ ee48679c
import copy
from collections import OrderedDict
from x2paddle.core.program import PaddleLayer
class BatchNormOpt:
def __init__(self):
pass
def run(self, graph):
print("Optimize: BatchNormOpt...")
layers = copy.deepcopy(graph.layers)
for layer_id, layer in layers.items():
if layer.kernel != "fluid.layers.elementwise_add":
continue
axis = layer.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
input_ids0 = graph.edges_in[layer_id]
mul_layer0 = graph.layers[input_ids0[0]]
sub_layer0 = graph.layers[input_ids0[1]]
if mul_layer0.kernel != "fluid.layers.elementwise_mul":
continue
if sub_layer0.kernel != "fluid.layers.elementwise_sub":
continue
axis = mul_layer0.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
axis = sub_layer0.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids0[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids0[1], [])) != 1:
continue
input_ids1 = graph.edges_in[input_ids0[0]]
nhwc_input = graph.layers[input_ids1[0]]
mul_layer1 = graph.layers[input_ids1[1]]
if mul_layer1.kernel != "fluid.layers.elementwise_mul":
continue
axis = mul_layer1.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids1[1], [])) != 2:
continue
input_ids2 = graph.edges_in[input_ids0[1]]
beta = graph.layers[input_ids2[0]]
mul_layer2 = graph.layers[input_ids2[1]]
if beta.kernel != "fluid.layers.create_parameter":
continue
axis = mul_layer2.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids2[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids2[1], [])) != 1:
continue
if beta.outputs[0] not in graph.parameters:
continue
beta_shape = graph.parameters[beta.outputs[0]].shape
if len(beta_shape) != 1:
continue
input_ids3 = graph.edges_in[input_ids2[1]]
mean = graph.layers[input_ids3[0]]
mul_layer3 = graph.layers[input_ids3[1]]
if mean.kernel != "fluid.layers.create_parameter":
continue
axis = mul_layer3.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids3[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids3[1], [])) != 2:
continue
if mul_layer3.id != mul_layer1.id:
continue
if mean.outputs[0] not in graph.parameters:
continue
mean_shape = graph.parameters[mean.outputs[0]].shape
if mean_shape != beta_shape:
continue
input_ids4 = graph.edges_in[input_ids3[1]]
rsqrt_layer = graph.layers[input_ids4[0]]
gamma = graph.layers[input_ids4[1]]
if rsqrt_layer.kernel != "fluid.layers.rsqrt":
continue
if gamma.kernel != "fluid.layers.create_parameter":
continue
if len(graph.edges_out.get(input_ids4[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids4[1], [])) != 1:
continue
if gamma.outputs[0] not in graph.parameters:
continue
gamma_shape = graph.parameters[gamma.outputs[0]].shape
if gamma_shape != beta_shape:
continue
input_ids5 = graph.edges_in[input_ids4[0]]
add_layer = graph.layers[input_ids5[0]]
if add_layer.kernel != "fluid.layers.elementwise_add":
continue
axis = add_layer.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids5[0], [])) != 1:
continue
input_ids6 = graph.edges_in[input_ids5[0]]
variance = graph.layers[input_ids6[0]]
other = graph.layers[input_ids6[1]]
if variance.kernel != "fluid.layers.create_parameter":
continue
if other.kernel != "fluid.layers.fill_constant":
continue
if len(graph.edges_out.get(input_ids6[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids6[1], [])) != 1:
continue
if variance.outputs[0] not in graph.parameters:
continue
variance_shape = graph.parameters[variance.outputs[0]].shape
if variance_shape != beta_shape:
continue
ids = set([
layer_id, mul_layer0.id, sub_layer0.id, mul_layer1.id, beta.id,
mul_layer2.id, mean.id, mul_layer2.id, rsqrt_layer.id, gamma.id,
add_layer.id, variance.id, other.id
])
for id in ids:
del graph.layers[id]
if id in graph.edges_in:
del graph.edges_in[id]
if id in graph.edges_out:
del graph.edges_out[id]
copy_layers = copy.deepcopy(graph.layers)
graph.layers = OrderedDict()
for k, v in copy_layers.items():
if k != nhwc_input.id:
graph.layers[k] = v
continue
graph.layers[k] = v
transpose0 = PaddleLayer(
id='{}_1'.format(k),
kernel="fluid.layers.transpose",
inputs={"x": v.outputs[0]},
outputs=["transpose_for_bn"],
perm=[0, 3, 1, 2])
bn = PaddleLayer(
id='{}_2'.format(k),
kernel="fluid.layers.batch_norm",
inputs={"input": "transpose_for_bn"},
outputs=layer.outputs,
epsilon=other.attrs["value"],
param_attr="'{}'".format(gamma.outputs[0]),
bias_attr="'{}'".format(beta.outputs[0]),
moving_mean_name="'{}'".format(mean.outputs[0]),
moving_variance_name="'{}'".format(variance.outputs[0]))
transpose1 = PaddleLayer(
id=layer_id,
kernel="fluid.layers.transpose",
inputs={"x": layer.outputs[0]},
outputs=layer.outputs,
perm=[0, 2, 3, 1])
graph.layers[transpose0.id] = transpose0
graph.layers[bn.id] = bn
graph.layers[transpose1.id] = transpose1
graph.build()
x2paddle/optimizer/tensorflow/bias.py 已删除 100644 → 0
浏览文件 @ ee48679c
import copy
class BiasOpt:
def __init__(self):
self.conv_layers = [
'fluid.layers.conv2d', 'fluid.layers.conv2d_transpose'
]
def run(self, graph):
print("Optimize: BiasOpt...")
layers = copy.deepcopy(graph.layers)
for layer_id, layer in layers.items():
if layer.kernel in self.conv_layers or layer.kernel == "fluid.layers.transpose":
if len(graph.edges_out.get(layer_id, [])) > 1:
continue
if layer.outputs[0] in graph.outputs:
continue
out_layer_id = graph.edges_out[layer_id][0]
if graph.layers[
out_layer_id].kernel != "fluid.layers.elementwise_add":
continue
if graph.layers[out_layer_id].attrs.get('axis', -1) != -1:
continue
in_layer_id = graph.edges_in[out_layer_id]
bias_layer_id = in_layer_id[1 - in_layer_id.index(layer_id)]
if graph.layers[
bias_layer_id].kernel != "fluid.layers.create_parameter":
continue
bias_layer = graph.layers[bias_layer_id]
if len(bias_layer.attrs['shape']) != 1:
continue
if len(graph.edges_out[bias_layer_id]) != 1:
continue
if layer.kernel == "fluid.layers.transpose":
if layer.attrs['perm'] != [0, 2, 3, 1]:
continue
in_layer_id = graph.edges_in[layer_id][0]
if graph.layers[in_layer_id].kernel not in self.conv_layers:
continue
if graph.layers[in_layer_id].attrs['bias_attr'] != False:
continue
if graph.layers[in_layer_id].outputs[0] in graph.outputs:
continue
if len(graph.edges_out[in_layer_id]) != 1:
continue
graph.layers[in_layer_id].attrs[
'bias_attr'] = bias_layer.attrs['name']
else:
graph.layers[layer_id].attrs[
'bias_attr'] = bias_layer.attrs['name']
bias_add_outs = graph.edges_out.get(out_layer_id, [])
bias_add_output = graph.layers[out_layer_id].outputs[0]
graph.del_layer(bias_layer_id)
graph.del_layer(out_layer_id)
for out in bias_add_outs:
for k, v in graph.layers[out].inputs.items():
if v == layer.outputs[0]:
graph.layers[out].inputs[k] = bias_add_output
graph.layers[layer_id].outputs[0] = bias_add_output
if layer.kernel == "fluid.layers.transpose":
in_layer_id = graph.edges_in[layer_id][0]
graph.layers[in_layer_id].outputs[0] = bias_add_output
graph.layers[layer_id].inputs['x'] = bias_add_output
x2paddle/optimizer/tensorflow/prelu.py 已删除 100644 → 0
浏览文件 @ ee48679c
import copy
import numpy as np
from collections import OrderedDict
from x2paddle.core.program import PaddleLayer
from x2paddle.core.util import *
class PReLUOpt:
def __init__(self):
pass
def run(self, graph):
print("Optimize: PReLUOpt...")
layers = copy.deepcopy(graph.layers)
for layer_id, layer in layers.items():
if layer.kernel != "fluid.layers.elementwise_add":
continue
axis = layer.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
input_ids0 = graph.edges_in[layer_id]
relu_layer0 = graph.layers[input_ids0[0]]
mul_layer0 = graph.layers[input_ids0[1]]
if relu_layer0.kernel != "fluid.layers.relu":
continue
if mul_layer0.kernel != "fluid.layers.elementwise_mul":
continue
axis = mul_layer0.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
if len(graph.edges_out.get(input_ids0[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids0[1], [])) != 1:
continue
input_ids1_0 = graph.edges_in[input_ids0[0]]
input_ids1_1 = graph.edges_in[input_ids0[1]]
fill_layer = graph.layers[input_ids1_1[1]]
mul_layer1 = graph.layers[input_ids1_1[0]]
if fill_layer.kernel != "fluid.layers.fill_constant":
continue
if mul_layer1.kernel != "fluid.layers.elementwise_mul":
continue
axis = mul_layer1.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids1_1[1], [])) != 1:
continue
if len(graph.edges_out.get(input_ids1_0[0], [])) != 3:
continue
input_ids2 = graph.edges_in[input_ids1_1[0]]
alpha = graph.layers[input_ids2[0]]
sub_layer = graph.layers[input_ids2[1]]
if alpha.kernel != "fluid.layers.create_parameter":
continue
if sub_layer.kernel != "fluid.layers.elementwise_sub":
continue
axis = sub_layer.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
if len(graph.edges_out.get(input_ids2[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids2[1], [])) != 1:
continue
if alpha.outputs[0] not in graph.parameters:
continue
input_ids3 = graph.edges_in[input_ids2[1]]
add_layer = graph.layers[input_ids3[0]]
abs_layer = graph.layers[input_ids3[1]]
if abs_layer.kernel != "fluid.layers.abs":
continue
if len(graph.edges_out.get(input_ids3[1], [])) != 1:
continue
ids = set([
layer.id, relu_layer0.id, mul_layer0.id, fill_layer.id, mul_layer1.id, alpha.id,
sub_layer.id, abs_layer.id])
for id in ids:
del graph.layers[id]
if id in graph.edges_in:
del graph.edges_in[id]
if id in graph.edges_out:
del graph.edges_out[id]
copy_layers = copy.deepcopy(graph.layers)
graph.layers = OrderedDict()
for k, v in copy_layers.items():
if k != add_layer.id:
graph.layers[k] = v
continue
graph.layers[k] = v
transpose0 = PaddleLayer(
id='{}_1'.format(k),
kernel="fluid.layers.transpose",
inputs={"x": v.outputs[0]},
outputs=["transpose_for_prelu"],
perm=[0, 3, 1, 2])
prelu = PaddleLayer(
id='{}_2'.format(k),
kernel="fluid.layers.prelu",
inputs={"x": "transpose_for_prelu"},
outputs=layer.outputs,
mode=string("channel"),
param_attr="'{}'".format(alpha.outputs[0]))
transpose1 = PaddleLayer(
id=layer_id,
kernel="fluid.layers.transpose",
inputs={"x": layer.outputs[0]},
outputs=layer.outputs,
perm=[0, 2, 3, 1])
graph.layers[transpose0.id] = transpose0
graph.layers[prelu.id] = prelu
graph.layers[transpose1.id] = transpose1
first_axis = graph.parameters[alpha.outputs[0]].shape[0]
graph.parameters[alpha.outputs[0]] = np.reshape(graph.parameters[alpha.outputs[0]], (1, first_axis, 1, 1))
graph.build()
\ No newline at end of file
x2paddle/optimizer/tensorflow/transpose.py 已删除 100644 → 0
浏览文件 @ ee48679c
import copy
import sys
class TransposeOpt:
def __init__(self):
self.image_layers = [
'fluid.layers.conv2d', 'fluid.layers.batch_norm',
'fluid.layers.conv2d_transpose', 'fluid.layers.resize_nearest',
'fluid.layers.resize_bilinear', 'fluid.layers.pool2d',
'fluid.layers.pad2d'
]
self.direct_layers = [
'fluid.layers.relu', 'fluid.layers.relu6', 'fluid.layers.abs',
'fluid.layers.sigmoid', 'fluid.layers.exp', 'fluid.layers.rsqrt',
'fluid.layers.swish_f32', 'fluid.layers.tanh',
'fluid.layers.softplus', 'fluid.layers.leaky_relu',
'fluid.layers.floor', 'fluid.layers.erf', 'fluid.layers.swish'
]
self.elementwise_layers = [
'fluid.layers.elementwise_add', 'fluid.layers.elementwise_sub',
'fluid.layers.elementwise_mul', 'fluid.layers.elementwise_div'
]
self.reduce_layers = [
'fluid.layers.reduce_mean', 'fluid.layers.reduce_all',
'fluid.layers.reduce_max', 'fluid.layers.reduce_any',
'fluid.layers.reduce_sum', 'fluid.layers.reduce_prod'
]
def get_transpose_num(self, graph):
count = 0
for layer_id, layer in graph.layers.items():
if layer.kernel == "fluid.layers.transpose":
count += 1
return count
def run(self, graph):
print("Optimize: TransposeOpt...")
total_layer_num = len(graph.layers)
scanned_layers = set()
optimized_transpose_layers = list()
optimized_reduce_layers = list()
optimized_concat_layers = list()
optimized_elementwise_layers = list()
def strip_transpose(_graph):
layers = copy.deepcopy(_graph.layers)
for layer_id, layer in layers.items():
if layer_id in scanned_layers:
continue
scanned_layers.add(layer_id)
percent = round(len(scanned_layers) / total_layer_num * 100, 2)
sys.stderr.write("\rOptimize Transpose Layers...{}%".format(
percent))
if layer.kernel != "fluid.layers.transpose":
continue
if layer.attrs["perm"] != [0, 2, 3, 1]:
continue
transpose_layers = list()
propagate_layers = list()
reduce_layers = list()
concat_layers = list()
# 此elementwise_layers专用于存储shape(4) + shape(1)的形式layer
elementwise_layers = list()
can_be_optimized = True
for out in _graph.edges_out.get(layer_id, []):
if _graph.layers[out].kernel == "fluid.layers.transpose":
if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
can_be_optimized = False
break
transpose_layers.append(out)
elif _graph.layers[out].kernel in self.elementwise_layers:
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.direct_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.reduce_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if not _graph.layers[out].attrs.get('keep_dim', False):
can_be_optimized = False
break
propagate_layers.append(out)
reduce_layers.append(out)
elif _graph.layers[out].kernel == "fluid.layers.concat":
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
propagate_layers.append(out)
concat_layers.append(out)
else:
can_be_optimized = False
break
visited_layers = set()
while len(propagate_layers) > 0 and can_be_optimized:
current_id = propagate_layers.pop(0)
visited_layers.add(current_id)
for out in _graph.edges_out.get(current_id, []):
if _graph.layers[
out].kernel == "fluid.layers.transpose":
if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
can_be_optimized = False
break
transpose_layers.append(out)
elif _graph.layers[
out].kernel in self.elementwise_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.direct_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.reduce_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if not _graph.layers[out].attrs.get('keep_dim',
False):
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
reduce_layers.append(out)
elif _graph.layers[out].kernel == "fluid.layers.concat":
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
concat_layers.append(out)
else:
can_be_optimized = False
break
for ipt in _graph.edges_in.get(current_id, []):
if _graph.layers[
current_id].kernel in self.elementwise_layers:
try:
x_shape = _graph.layers[
current_id].input_shapes['x']
y_shape = _graph.layers[
current_id].input_shapes['y']
if _graph.layers[ipt].outputs[
0] == _graph.layers[current_id].inputs[
'x']:
if len(x_shape) <= 1:
elementwise_layers.append(current_id)
continue
elif _graph.layers[ipt].outputs[
0] == _graph.layers[current_id].inputs[
'y']:
if len(y_shape) <= 1:
elementwise_layers.append(current_id)
continue
else:
raise Exception(
"Unexcepted situation happend while optimizing transpose"
)
except Exception as e:
can_be_optimized = False
break
if _graph.layers[
ipt].kernel == "fluid.layers.transpose":
if _graph.layers[ipt].attrs["perm"] != [0, 2, 3, 1]:
can_be_optimized = False
break
if ipt not in visited_layers:
transpose_layers.append(ipt)
elif _graph.layers[
ipt].kernel in self.elementwise_layers:
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
elif _graph.layers[ipt].kernel in self.direct_layers:
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
elif _graph.layers[ipt].kernel in self.reduce_layers:
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if not _graph.layers[ipt].attrs.get('keep_dim',
False):
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
reduce_layers.append(ipt)
elif _graph.layers[ipt].kernel == "fluid.layers.concat":
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
concat_layers.append(ipt)
else:
can_be_optimized = False
break
if not can_be_optimized:
break
if not can_be_optimized:
continue
transpose_layers.append(layer_id)
transpose_layers = list(set(transpose_layers))
for l in transpose_layers:
if graph.layers[l].outputs[0] in graph.outputs:
can_be_optimized = False
break
if not can_be_optimized:
continue
for l in transpose_layers:
_graph.del_layer(l)
optimized_transpose_layers.extend(transpose_layers)
optimized_reduce_layers.extend(reduce_layers)
optimized_concat_layers.extend(concat_layers)
optimized_elementwise_layers.extend(elementwise_layers)
return True
return False
before_transpose_num = self.get_transpose_num(graph)
opt_graph = copy.deepcopy(graph)
total_layer_num = len(opt_graph.layers)
while strip_transpose(opt_graph):
pass
for layer_id in list(set(optimized_transpose_layers)):
graph.del_layer(layer_id)
for layer_id in list(set(optimized_reduce_layers)):
dim = graph.layers[layer_id].attrs.get('dim', None)
if dim is not None:
for i in range(len(dim)):
dim[i] = [0, 2, 3, 1][dim[i]]
graph.layers[layer_id].attrs['dim'] = dim
for layer_id in list(set(optimized_concat_layers)):
axis = graph.layers[layer_id].attrs.get('axis', 0)
graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis]
for layer_id in list(set(optimized_elementwise_layers)):
axis = graph.layers[layer_id].attrs.get('axis', -1)
graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis]
current_transpose_num = self.get_transpose_num(graph)
print(
"\nTranspose layers optimized, before: transpose_num={}, after: transpose_num={}".
format(before_transpose_num, current_transpose_num))
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