# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import csv
import logging
__all__ = ['ChannelDependency', 'GroupDependency',
'CatPaddingDependency', 'InputChannelDependency']
CONV_TYPE = 'aten::_convolution'
ADD_TYPES = ['aten::add', 'aten::add_']
CAT_TYPE = 'aten::cat'
logger = logging.getLogger('Shape_Dependency')
RESHAPE_OPS = [CAT_TYPE, 'aten::view',
'aten::reshape', 'aten::flatten', 'aten::mean']
class Dependency:
def __init__(self, model=None, dummy_input=None, traced_model=None):
"""
Build the graph for the model.
"""
from nni.common.graph_utils import TorchModuleGraph
# check if the input is legal
if traced_model is None:
# user should provide model & dummy_input to trace
# the model or a already traced model
assert model is not None and dummy_input is not None
self.graph = TorchModuleGraph(model, dummy_input, traced_model)
self.dependency = dict()
self.build_dependency()
def build_dependency(self):
raise NotImplementedError
def export(self, filepath):
raise NotImplementedError
[docs]class ChannelDependency(Dependency):
def __init__(self, model=None, dummy_input=None, traced_model=None):
"""
This model analyze the channel dependencies between the conv
layers in a model.
Parameters
----------
model : torch.nn.Module
The model to be analyzed.
data : torch.Tensor
The example input data to trace the network architecture.
traced_model : torch._C.Graph
if we alreay has the traced graph of the target model, we donnot
need to trace the model again.
"""
super(ChannelDependency, self).__init__(
model, dummy_input, traced_model)
def _get_parent_layers(self, node):
"""
Find the nearest father conv layers for the target node.
Parameters
---------
node : torch._C.Node
target node.
Returns
-------
parent_layers: list
nearest father conv/linear layers for the target worknode.
"""
parent_layers = []
queue = []
queue.append(node)
while queue:
curnode = queue.pop(0)
if curnode.op_type == 'Conv2d' or curnode.op_type == 'Linear' or curnode.op_type == 'ConvTranspose2d':
# find the first met conv
parent_layers.append(curnode.name)
continue
parents = self.graph.find_predecessors(curnode.unique_name)
parents = [self.graph.name_to_node[name] for name in parents]
for parent in parents:
queue.append(parent)
return parent_layers
[docs] def build_dependency(self):
"""
Build the channel dependency for the conv layers
in the model.
"""
# unpack the tuple/list manually before analyze the
# channel dependency
self.graph.unpack_manually()
for node in self.graph.nodes_py.nodes_op:
parent_layers = []
# find the node that contains aten::add
# or aten::cat operations
if node.op_type in ADD_TYPES:
parent_layers = self._get_parent_layers(node)
elif node.op_type == CAT_TYPE:
# To determine if this cat operation will introduce channel
# dependency, we need the specific input parameters of the cat
# opertion. To get the input parameters of the cat opertion, we
# need to traverse all the cpp_nodes included by this NodePyGroup,
# because, TorchModuleGraph merges the important nodes and the adjacent
# unimportant nodes (nodes started with prim::attr, for example) into a
# NodepyGroup.
cat_dim = None
for cnode in node.node_cpps:
if cnode.kind() == CAT_TYPE:
cat_dim = list(cnode.inputs())[1].toIValue()
break
if cat_dim != 1:
parent_layers = self._get_parent_layers(node)
dependency_set = set(parent_layers)
# merge the dependencies
for parent in parent_layers:
if parent in self.dependency:
dependency_set.update(self.dependency[parent])
# save the dependencies
for _node in dependency_set:
self.dependency[_node] = dependency_set
[docs] def export(self, filepath):
"""
export the channel dependencies as a csv file.
The layers at the same line have output channel
dependencies with each other. For example,
layer1.1.conv2, conv1, and layer1.0.conv2 have
output channel dependencies with each other, which
means the output channel(filters) numbers of these
three layers should be same with each other, otherwise
the model may has shape conflict.
Output example:
Dependency Set,Convolutional Layers
Set 1,layer1.1.conv2,layer1.0.conv2,conv1
Set 2,layer1.0.conv1
Set 3,layer1.1.conv1
"""
header = ['Dependency Set', 'Layers']
setid = 0
visited = set()
with open(filepath, 'w') as csvf:
csv_w = csv.writer(csvf, delimiter=',')
csv_w.writerow(header)
for node in self.graph.nodes_py.nodes_op:
if node.op_type != 'Conv2d' or node in visited:
continue
setid += 1
row = ['Set %d' % setid]
if node.name not in self.dependency:
visited.add(node)
row.append(node.name)
else:
for other in self.dependency[node.name]:
visited.add(self.graph.name_to_node[other])
row.append(other)
csv_w.writerow(row)
@property
def dependency_sets(self):
"""
Get the list of the dependency set.
Returns
-------
dependency_sets : list
list of the dependency sets. For example,
[set(['conv1', 'conv2']), set(['conv3', 'conv4'])]
"""
d_sets = []
visited = set()
for node in self.graph.nodes_py.nodes_op:
if node.op_type != 'Conv2d' or node in visited:
continue
tmp_set = set()
if node.name not in self.dependency:
visited.add(node)
tmp_set.add(node.name)
else:
for other in self.dependency[node.name]:
visited.add(self.graph.name_to_node[other])
tmp_set.add(other)
d_sets.append(tmp_set)
return d_sets
def reshape_break_channel_dependency(op_node):
"""
The reshape operations such as (reshape, view, flatten) may break
the channel dependency. We need to check the input parameters of
these reshape operations to check if this reshape node will break
the channel dependency. However, it's complicated to analyze the the input
parameters for each reshape function and infer if it will break the channel
dependency. So currently, we just check if the input channel and the output
channel is the same, if so, then we can say the original reshape function
doesn't want to change the number of the channels, which means the channel
dependency is not broken. In contrast, the original reshap operation wants
to change the number of channels, so it breaks the channel dependency.
Parameters
----------
opnode: NodePyOP
A Op node of the graph.
Returns
-------
bool
If this operation will break the channel dependency.
"""
in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel
class InputChannelDependency(ChannelDependency):
"""
Some pruners may prune the input channel of the convolutional
layers. While pruning the input channel of the convolutional layers,
the layers that share the same input tensor should prune the same
channels, and we say these layers that share the same input tensor/channel
has the input channel dependency. If we only prune the input channel of one
layer in the dependency set, there will be a shape conflict for the other
layers in the same dependency set, which may trigger a runtime error.
Here we judge whether the application will truncate the dependency by analyzing
whether the number of channels before and after the operation has changed.
If not, the input channel dependency will be passed to the following nodes.
"""
def __init__(self, model, dummy_input=None, traced_model=None):
"""
This model analyze the input channel dependencies between the conv
layers in a model.
Parameters
----------
model : torch.nn.Module
The model to be analyzed.
data : torch.Tensor
The example input data to trace the network architecture.
traced_model : torch._C.Graph
if we alreay has the traced graph of the target model, we donnot
need to trace the model again.
"""
super(InputChannelDependency, self).__init__(
model, dummy_input, traced_model)
def _get_following_convs(self, tensor):
queue = []
key_layers = []
queue.extend(self.graph.input_to_node[tensor])
while queue:
curnode = queue.pop(0)
if curnode.op_type == 'Conv2d' or curnode.op_type == 'Linear' or curnode.op_type == 'ConvTranspose2d':
# find the first met conv
key_layers.append(curnode.name)
continue
elif curnode.op_type in RESHAPE_OPS:
# check if the reshape operation will break the channel dependency
if reshape_break_channel_dependency(curnode):
# reshape operations also breaks the dependency relationship
continue
successors = self.graph.find_successors(curnode.unique_name)
successors = [self.graph.name_to_node[name] for name in successors]
for layer in successors:
queue.append(layer)
return key_layers
def build_dependency(self):
"""
Build the input channel dependencies.
The `InputChannelDependency` indicates the layers that have
dependencies when pruning the input channel of the conv layers.
In contrast, `ChannelDependency` indicates the dependent layers
when pruning the output channles of conv layers (for example, L1FilterPruner).
"""
# unpack the tuple or list manually
self.graph.unpack_manually()
for tensor in self.graph.input_to_node:
# start from this tensor, find all the conv layers that
# take this tensor as input. Similar to the `ChannelDependency`
# the conv layer will truncate the dependencies
layers = self._get_following_convs(tensor)
dependency_set = set(layers)
for layer in layers:
if layer in self.dependency:
dependency_set.update(self.dependency[layer])
for layer in dependency_set:
self.dependency[layer] = dependency_set
class CatPaddingDependency(ChannelDependency):
def __init__(self, model=None, dummy_input=None, traced_model=None):
super(CatPaddingDependency, self).__init__(
model, dummy_input, traced_model)
def build_dependency(self):
"""
Build the cat padding dependencies.
If the output features of several layers are stitched together
by cat operation, then these layers have cat padding dependencies.
This is because when inferring the cat mask, we need all the input
masks for the cat operation. At this time we need to know the source
of all input vectors of a cat operation.
"""
for node in self.graph.nodes_py.nodes_op:
parent_layers = []
if node.op_type == CAT_TYPE:
parent_layers = self._get_parent_layers(node)
dependency_set = set(parent_layers)
# merge the dependencies
for parent in parent_layers:
if parent in self.dependency:
dependency_set.update(self.dependency[parent])
# save the dependencies
for _node in dependency_set:
self.dependency[_node] = dependency_set
@property
def dependency_sets(self):
d_sets = []
visited = set()
for nodename in self.dependency:
if nodename in visited:
continue
d_sets.append(self.dependency[nodename])
return d_sets
def export(self, filepath):
"""
Export the dependencies into a file.
In the output file, each line contains a set of layers
whose output features are stitched together by the cat
operation.
output example:
Dependency Set, Layers
set1, Conv1, Conv2
set2, Conv3, Conv4
"""
header = ['Dependency Set', 'Layers']
setid = 0
with open(filepath, 'w') as csvf:
csv_w = csv.writer(csvf, delimiter=',')
csv_w.writerow(header)
for layers in self.dependency_sets:
setid += 1
row = ['Set %d' % setid]
row.extend(list(layers))
csv_w.writerow(row)
[docs]class GroupDependency(Dependency):
def __init__(self, model=None, dummy_input=None, traced_model=None):
"""
This model analyze the group dependencis between the conv
layers in a model.
Parameters
----------
model : torch.nn.Module
The model to be analyzed.
data : torch.Tensor
The example input data to trace the network architecture.
traced_model : torch._C.Graph
if we alreay has the traced graph of the target model, we donnot
need to trace the model again.
"""
super(GroupDependency, self).__init__(model, dummy_input, traced_model)
def _get_parent_convs(self, node):
"""
Find the nearest father conv layers for the target node.
Parameters
---------
node : torch._C.Node
target node.
Returns
-------
parent_layers : list
nearest father conv layers for the target node. Due to the group
dependency only exists between the conv layers, so we only find
the parent conv layers.
"""
parent_layers = []
# the input node is a Conv node
predeessors = self.graph.find_predecessors(node.unique_name)
predeessors = [self.graph.name_to_node[x] for x in predeessors]
queue = predeessors
while queue:
curnode = queue.pop(0)
if curnode.op_type == 'Conv2d' or curnode.op_type == 'ConvTranspose2d':
# find the first met conv
parent_layers.append(curnode.name)
continue
parents = self.graph.find_predecessors(curnode.unique_name)
parents = [self.graph.name_to_node[name] for name in parents]
for parent in parents:
queue.append(parent)
return parent_layers
def _get_conv_groups(self, node_group):
"""
Get the number of groups for a convolutional layer.
Parameters
----------
node_group : NodePyGroup
target node.
Returns
-------
group : int
the number of the groups of the target conv layer.
"""
cpp_conv = list(filter(lambda x: x.kind() ==
CONV_TYPE, node_group.node_cpps))
assert len(cpp_conv) == 1
cpp_conv = cpp_conv[0]
inputs = list(cpp_conv.inputs())
# get the number of the group from the input parameters
group = inputs[8].toIValue()
return group
[docs] def build_dependency(self):
"""
Build the channel dependency for the conv layers
in the model. This function return the group number
of each conv layers. Note that, here, the group count
of conv layers may be larger than their originl groups.
This is because that the input channel will also be grouped
for the group conv layers. To make this clear, assume we
have two group conv layers: conv1(group=2), conv2(group=4).
conv2 takes the output features of conv1 as input.
Then we have to the filters of conv1 can still be
divided into 4 groups after filter pruning, because
the input channels of conv2 shoule be divided into
4 groups.
Returns
-------
self.dependency : dict
key: the name of conv layers, value: the minimum value that the number of
filters should be divisible to.
"""
for node in self.graph.nodes_py.nodes_op:
if node.op_type == 'Conv2d' or node.op_type == 'ConvTranspose2d':
group = self._get_conv_groups(node)
if node.name in self.dependency:
# the conv layer whose group is larger than 1 will require that
# it's number of output channel to be divisible by the number of group.
self.dependency[node.name] = max(
self.dependency[node.name], group)
else:
self.dependency[node.name] = group
if group > 1:
# for the conv layer whose group is larger than 1, it will require the number
# of output channels of their parent conv layer to be divisible by group.
parent_convs = self._get_parent_convs(node)
for parent in parent_convs:
if parent in self.dependency:
self.dependency[parent] = max(
self.dependency[parent], group)
else:
self.dependency[parent] = group
return self.dependency
[docs] def export(self, filepath):
"""
export the group dependency to a csv file.
Each line describes a convolution layer, the
first part of each line is the Pytorch module
name of the conv layer. The second part of each
line is the group count of the filters in this layer.
Note that, the group count may be larger than this
layers original group number.
output example:
Conv layer, Groups
Conv1, 1
Conv2, 2
Conv3, 4
"""
header = ['Conv Layer Name', 'Group']
with open(filepath, 'w') as csvf:
csv_w = csv.writer(csvf, delimiter=',')
csv_w.writerow(header)
for name in self.dependency:
group = self.dependency[name]
csv_w.writerow([name, group])
@property
def dependency_sets(self):
return self.dependency