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prune_walker.py
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prune_walker.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import numpy as np
from ..core import Registry
from ..common import get_logger
__all__ = ["PRUNE_WORKER", "conv2d"]
_logger = get_logger(__name__, level=logging.INFO)
PRUNE_WORKER = Registry('prune_worker')
class PruneWorker(object):
def __init__(self, op, pruned_params=[], visited={}):
"""
A wrapper of operator used to infer the information of all the related variables.
Args:
op(Operator): The operator to be pruned.
pruned_params(list): The list to store the information of pruning that infered by walker.
visited(dict): The auxiliary dict to record the visited operators and variables. The key is a encoded string of operator id and variable name.
Return: A instance of PruneWalker.
"""
self.op = op
self.pruned_params = pruned_params
self.visited = visited
def prune(self, var, pruned_axis, pruned_idx):
"""
Infer the shape of variables related with current operator, predecessor and successor.
It will search the graph to find all varibles related with `var` and record the information of pruning.
Args:
var(Variable): The root variable of searching. It can be the input or output of current operator.
pruned_axis(int): The axis to be pruned of root variable.
pruned_idx(int): The indexes to be pruned in `pruned_axis` of root variable.
"""
if self._visit(var, pruned_axis):
self._prune(var, pruned_axis, pruned_idx)
def _visit(self, var, pruned_axis):
key = "_".join([str(self.op.idx()), var.name()])
if pruned_axis not in self.visited:
self.visited[pruned_axis] = {}
if key in self.visited[pruned_axis]:
return False
else:
self.visited[pruned_axis][key] = True
return True
def _prune(self, var, pruned_axis, pruned_idx):
raise NotImplementedError('Abstract method.')
def _prune_op(self, op, var, pruned_axis, pruned_idx, visited=None):
if op.type().endswith("_grad"):
return
if visited is not None:
self.visited = visited
cls = PRUNE_WORKER.get(op.type())
assert cls is not None, "The walker of {} is not registered.".format(
op.type())
_logger.debug("\nfrom: {}\nto: {}\npruned_axis: {}; var: {}".format(
self.op, op, pruned_axis, var.name()))
walker = cls(op,
pruned_params=self.pruned_params,
visited=self.visited)
walker.prune(var, pruned_axis, pruned_idx)
@PRUNE_WORKER.register
class conv2d(PruneWorker):
def __init__(self, op, pruned_params, visited={}):
super(conv2d, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
data_format = self.op.attr("data_format")
channel_axis = 1
if data_format == "NHWC":
channel_axis = 3
if var in self.op.inputs("Input"):
assert pruned_axis == channel_axis, "The Input of conv2d can only be pruned at channel axis, but got {}; var: {}".format(
pruned_axis, var.name())
filter_var = self.op.inputs("Filter")[0]
self._visit(filter_var, 1)
self.pruned_params.append((filter_var, 1, pruned_idx))
for op in filter_var.outputs():
self._prune_op(op, filter_var, 1, pruned_idx)
elif var in self.op.inputs("Filter"):
assert pruned_axis in [0, 1]
self.pruned_params.append((var, pruned_axis, pruned_idx))
for op in var.outputs():
self._prune_op(op, var, pruned_axis, pruned_idx)
if pruned_axis == 0:
if len(self.op.inputs("Bias")) > 0:
self.pruned_params.append(
(self.op.inputs("Bias"), channel_axis, pruned_idx))
output_var = self.op.outputs("Output")[0]
self._visit(output_var, channel_axis)
next_ops = output_var.outputs()
for op in next_ops:
self._prune_op(op, output_var, channel_axis, pruned_idx)
elif pruned_axis == 1:
input_var = self.op.inputs("Input")[0]
self._visit(input_var, channel_axis)
pre_ops = input_var.inputs()
for op in pre_ops:
self._prune_op(op, input_var, channel_axis, pruned_idx)
elif var in self.op.outputs("Output"):
assert pruned_axis == channel_axis, "pruned_axis: {}; var: {}".format(
pruned_axis, var.name())
filter_var = self.op.inputs("Filter")[0]
self._visit(filter_var, 0)
self.pruned_params.append((filter_var, 0, pruned_idx))
for op in filter_var.outputs():
self._prune_op(op, filter_var, 0, pruned_idx)
if len(self.op.inputs("Bias")) > 0:
self.pruned_params.append(
(self.op.inputs("Bias")[0], channel_axis, pruned_idx))
output_var = self.op.outputs("Output")[0]
next_ops = output_var.outputs()
for op in next_ops:
self._prune_op(op, output_var, channel_axis, pruned_idx)
@PRUNE_WORKER.register
class batch_norm(PruneWorker):
def __init__(self, op, pruned_params, visited):
super(batch_norm, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
if (var not in self.op.outputs("Y")) and (
var not in self.op.inputs("X")):
return
if var in self.op.outputs("Y"):
in_var = self.op.inputs("X")[0]
self._visit(in_var, pruned_axis)
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, pruned_idx)
for param in ["Scale", "Bias", "Mean", "Variance"]:
param_var = self.op.inputs(param)[0]
for op in param_var.outputs():
self._prune_op(op, param_var, 0, pruned_idx)
self.pruned_params.append((param_var, 0, pruned_idx))
out_var = self.op.outputs("Y")[0]
self._visit(out_var, pruned_axis)
next_ops = out_var.outputs()
for op in next_ops:
self._prune_op(op, out_var, pruned_axis, pruned_idx)
class elementwise_op(PruneWorker):
def __init__(self, op, pruned_params, visited):
super(elementwise_op, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
axis = self.op.attr("axis")
if axis == -1: # TODO
axis = 0
if var in self.op.outputs("Out"):
for name in ["X", "Y"]:
actual_axis = pruned_axis
if name == "Y":
actual_axis = pruned_axis - axis
in_var = self.op.inputs(name)[0]
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, actual_axis, pruned_idx)
else:
if var in self.op.inputs("X"):
in_var = self.op.inputs("Y")[0]
if in_var.is_parameter():
self.pruned_params.append(
(in_var, pruned_axis - axis, pruned_idx))
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis - axis, pruned_idx)
elif var in self.op.inputs("Y"):
in_var = self.op.inputs("X")[0]
pre_ops = in_var.inputs()
pruned_axis = pruned_axis + axis
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, pruned_idx)
out_var = self.op.outputs("Out")[0]
self._visit(out_var, pruned_axis)
next_ops = out_var.outputs()
for op in next_ops:
self._prune_op(op, out_var, pruned_axis, pruned_idx)
@PRUNE_WORKER.register
class elementwise_add(elementwise_op):
def __init__(self, op, pruned_params, visited):
super(elementwise_add, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class elementwise_sub(elementwise_op):
def __init__(self, op, pruned_params, visited):
super(elementwise_sub, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class elementwise_mul(elementwise_op):
def __init__(self, op, pruned_params, visited):
super(elementwise_mul, self).__init__(op, pruned_params, visited)
class activation(PruneWorker):
def __init__(self, op, pruned_params, visited):
super(activation, self).__init__(op, pruned_params, visited)
self.input_name = "X"
self.output_name = "Out"
def _prune(self, var, pruned_axis, pruned_idx):
if var in self.op.outputs(self.output_name):
in_var = self.op.inputs(self.input_name)[0]
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, pruned_idx)
out_var = self.op.outputs(self.output_name)[0]
self._visit(out_var, pruned_axis)
next_ops = out_var.outputs()
for op in next_ops:
self._prune_op(op, out_var, pruned_axis, pruned_idx)
@PRUNE_WORKER.register
class uniform_random_batch_size_like(activation):
def __init__(self, op, pruned_params, visited):
super(uniform_random_batch_size_like, self).__init__(op, pruned_params,
visited)
self.input_name = "Input"
self.output_name = "Out"
@PRUNE_WORKER.register
class bilinear_interp(activation):
def __init__(self, op, pruned_params, visited):
super(bilinear_interp, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class nearest_interp(activation):
def __init__(self, op, pruned_params, visited):
super(nearest_interp, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class relu(activation):
def __init__(self, op, pruned_params, visited):
super(relu, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class leaky_relu(activation):
def __init__(self, op, pruned_params, visited):
super(leaky_relu, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class floor(activation):
def __init__(self, op, pruned_params, visited):
super(floor, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class relu6(activation):
def __init__(self, op, pruned_params, visited):
super(relu6, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class pool2d(activation):
def __init__(self, op, pruned_params, visited):
super(pool2d, self).__init__(op, pruned_params, visited)
@PRUNE_WORKER.register
class sum(PruneWorker):
def __init__(self, op, pruned_params, visited):
super(sum, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
if var in self.op.outputs("Out"):
for in_var in self.op.inputs("X"):
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, pruned_idx)
elif var in self.op.inputs("X"):
for in_var in self.op.inputs("X"):
if in_var != var:
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, pruned_idx)
out_var = self.op.outputs("Out")[0]
self._visit(out_var, pruned_axis)
next_ops = out_var.outputs()
for op in next_ops:
self._prune_op(op, out_var, pruned_axis, pruned_idx)
@PRUNE_WORKER.register
class concat(PruneWorker):
def __init__(self, op, pruned_params, visited):
super(concat, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
idx = []
axis = self.op.attr("axis")
if var in self.op.outputs("Out"):
start = 0
if axis == pruned_axis:
for _, in_var in enumerate(self.op.inputs("X")):
idx = []
for i in pruned_idx:
r_idx = i - start
if r_idx < in_var.shape()[pruned_axis] and r_idx >= 0:
idx.append(r_idx)
start += in_var.shape()[pruned_axis]
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, idx)
idx = pruned_idx[:]
else:
for _, in_var in enumerate(self.op.inputs("X")):
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, pruned_axis, pruned_idx)
elif var in self.op.inputs("X"):
if axis == pruned_axis:
idx = []
start = 0
for v in self.op.inputs("X"):
if v.name() == var.name():
idx = [i + start for i in pruned_idx]
else:
start += v.shape()[pruned_axis]
out_var = self.op.outputs("Out")[0]
self._visit(out_var, pruned_axis)
next_ops = out_var.outputs()
for op in next_ops:
self._prune_op(op, out_var, pruned_axis, idx, visited={})
else:
for v in self.op.inputs("X"):
for op in v.inputs():
self._prune_op(op, v, pruned_axis, pruned_idx)
out_var = self.op.outputs("Out")[0]
self._visit(out_var, pruned_axis)
next_ops = out_var.outputs()
for op in next_ops:
self._prune_op(op, out_var, pruned_axis, pruned_idx)
@PRUNE_WORKER.register
class depthwise_conv2d(PruneWorker):
def __init__(self, op, pruned_params, visited={}):
super(depthwise_conv2d, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
data_format = self.op.attr("data_format")
channel_axis = 1
if data_format == "NHWC":
channel_axis = 3
if var in self.op.inputs("Input"):
assert pruned_axis == channel_axis, "The Input of conv2d can only be pruned at channel axis, but got {}".format(
pruned_axis)
filter_var = self.op.inputs("Filter")[0]
self.pruned_params.append((filter_var, 0, pruned_idx))
self._visit(filter_var, 0)
new_groups = filter_var.shape()[0] - len(pruned_idx)
self.op.set_attr("groups", new_groups)
for op in filter_var.outputs():
self._prune_op(op, filter_var, 0, pruned_idx)
output_var = self.op.outputs("Output")[0]
next_ops = output_var.outputs()
for op in next_ops:
self._prune_op(op, output_var, channel_axis, pruned_idx)
elif var in self.op.inputs("Filter"):
assert pruned_axis in [0]
if pruned_axis == 0:
if len(self.op.inputs("Bias")) > 0:
self.pruned_params.append(
(self.op.inputs("Bias"), channel_axis, pruned_idx))
self.pruned_params.append((var, 0, pruned_idx))
new_groups = var.shape()[0] - len(pruned_idx)
self.op.set_attr("groups", new_groups)
for op in var.outputs():
self._prune_op(op, var, 0, pruned_idx)
output_var = self.op.outputs("Output")[0]
self._visit(output_var, channel_axis)
next_ops = output_var.outputs()
for op in next_ops:
self._prune_op(op, output_var, channel_axis, pruned_idx)
for op in var.outputs():
self._prune_op(op, var, pruned_axis, pruned_idx)
elif var in self.op.outputs("Output"):
assert pruned_axis == channel_axis
filter_var = self.op.inputs("Filter")[0]
self.pruned_params.append((filter_var, 0, pruned_idx))
self._visit(filter_var, 0)
new_groups = filter_var.shape()[0] - len(pruned_idx)
op.set_attr("groups", new_groups)
for op in filter_var.outputs():
self._prune_op(op, filter_var, 0, pruned_idx)
if len(self.op.inputs("Bias")) > 0:
self.pruned_params.append(
(self.op.inputs("Bias")[0], channel_axis, pruned_idx))
in_var = self.op.inputs("Input")[0]
self._visit(in_var, channel_axis)
pre_ops = in_var.inputs()
for op in pre_ops:
self._prune_op(op, in_var, channel_axis, pruned_idx)
output_var = self.op.outputs("Output")[0]
next_ops = output_var.outputs()
for op in next_ops:
self._prune_op(op, output_var, channel_axis, pruned_idx)
@PRUNE_WORKER.register
class mul(PruneWorker):
def __init__(self, op, pruned_params, visited={}):
super(mul, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
if var in self.op.inputs("X"):
assert pruned_axis == 1, "The Input of conv2d can only be pruned at axis 1, but got {}".format(
pruned_axis)
idx = []
feature_map_size = var.shape()[2] * var.shape()[3]
range_idx = np.array(range(feature_map_size))
for i in pruned_idx:
idx += list(range_idx + i * feature_map_size)
param_var = self.op.inputs("Y")[0]
self.pruned_params.append((param_var, 0, idx))
for op in param_var.outputs():
self._prune_op(op, param_var, 0, pruned_idx)
@PRUNE_WORKER.register
class scale(PruneWorker):
def __init__(self, op, pruned_params, visited={}):
super(scale, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
if var in self.op.inputs("X"):
out_var = self.op.outputs("Out")[0]
for op in out_var.outputs():
self._prune_op(op, out_var, pruned_axis, pruned_idx)
elif var in self.op.outputs("Out"):
in_var = self.op.inputs("X")[0]
for op in in_var.inputs():
self._prune_op(op, in_var, pruned_axis, pruned_idx)
@PRUNE_WORKER.register
class momentum(PruneWorker):
def __init__(self, op, pruned_params, visited={}):
super(momentum, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
if var in self.op.inputs("Param"):
_logger.debug("pruning momentum, var:{}".format(var.name()))
velocity_var = self.op.inputs("Velocity")[0]
self.pruned_params.append((velocity_var, pruned_axis, pruned_idx))
@PRUNE_WORKER.register
class adam(PruneWorker):
def __init__(self, op, pruned_params, visited={}):
super(adam, self).__init__(op, pruned_params, visited)
def _prune(self, var, pruned_axis, pruned_idx):
if var in self.op.inputs("Param"):
_logger.debug("pruning momentum, var:{}".format(var.name()))
moment1_var = self.op.inputs("Moment1")[0]
self.pruned_params.append((moment1_var, pruned_axis, pruned_idx))
moment2_var = self.op.inputs("Moment2")[0]
self.pruned_params.append((moment2_var, pruned_axis, pruned_idx))