from __future__ import absolute_import, division, print_function, unicode_literals
import torch
import copy
import torch.nn.intrinsic.modules.fused as torch_fused
def fuse_conv_bn(conv, bn):
r"""Given the conv and bn modules, fuses them and returns the fused module
Args:
conv: Module instance of type conv2d/conv3d
bn: Spatial BN instance that needs to be fused with the conv
Examples::
>>> m1 = nn.Conv2d(10, 20, 3)
>>> b1 = nn.BatchNorm2d(20)
>>> m2 = fuse_conv_bn(m1, b1)
"""
assert(conv.training == bn.training),\
"Conv and BN both must be in the same mode (train or eval)."
is_3d = isinstance(conv, torch.nn.Conv3d)
if conv.training:
assert bn.num_features == conv.out_channels, 'Output channel of Conv2d must match num_features of BatchNorm2d'
assert bn.affine, 'Only support fusing BatchNorm2d with affine set to True'
assert bn.track_running_stats, 'Only support fusing BatchNorm2d with tracking_running_stats set to True'
return torch.nn.intrinsic.ConvBn3d(conv, bn) if is_3d \
else torch.nn.intrinsic.ConvBn2d(conv, bn)
else:
return torch.nn.utils.fuse_conv_bn_eval(conv, bn)
def fuse_conv_bn_relu(conv, bn, relu):
r"""Given the conv and bn modules, fuses them and returns the fused module
Args:
conv: Module instance of type conv2d/conv3d
bn: Spatial BN instance that needs to be fused with the conv
Examples::
>>> m1 = nn.Conv2d(10, 20, 3)
>>> b1 = nn.BatchNorm2d(20)
>>> m2 = fuse_conv_bn(m1, b1)
"""
assert(conv.training == bn.training == relu.training),\
"Conv and BN both must be in the same mode (train or eval)."
if conv.training:
map_to_fused_module_train = {
torch.nn.Conv2d: torch_fused.ConvBnReLU2d,
torch.nn.Conv3d: torch_fused.ConvBnReLU3d,
}
assert bn.num_features == conv.out_channels, 'Output channel of Conv must match num_features of BatchNorm'
assert bn.affine, 'Only support fusing BatchNorm with affine set to True'
assert bn.track_running_stats, 'Only support fusing BatchNorm with tracking_running_stats set to True'
fused_module = map_to_fused_module_train.get(type(conv))
if fused_module is not None:
return fused_module(conv, bn, relu)
else:
raise NotImplementedError("Cannot fuse train modules: {}".format((conv, bn, relu)))
else:
map_to_fused_module_eval = {
torch.nn.Conv1d: torch_fused.ConvReLU1d,
torch.nn.Conv2d: torch_fused.ConvReLU2d,
torch.nn.Conv3d: torch_fused.ConvReLU3d,
}
fused_module = map_to_fused_module_eval[type(conv)]
if fused_module is not None:
return fused_module(torch.nn.utils.fusion.fuse_conv_bn_eval(conv, bn), relu)
else:
raise NotImplementedError("Cannot fuse eval modules: {}".format((conv, bn, relu)))
# Generalization of getattr
def _get_module(model, submodule_key):
tokens = submodule_key.split('.')
cur_mod = model
for s in tokens:
cur_mod = getattr(cur_mod, s)
return cur_mod
# Generalization of setattr
def _set_module(model, submodule_key, module):
tokens = submodule_key.split('.')
sub_tokens = tokens[:-1]
cur_mod = model
for s in sub_tokens:
cur_mod = getattr(cur_mod, s)
setattr(cur_mod, tokens[-1], module)
def fuse_known_modules(mod_list):
r"""Returns a list of modules that fuses the operations specified
in the input module list.
Fuses only the following sequence of modules:
conv, bn
conv, bn, relu
conv, relu
linear, relu
For these sequences, the first element in the output module list performs
the fused operation. The rest of the elements are set to nn.Identity()
"""
OP_LIST_TO_FUSER_METHOD = {
(torch.nn.Conv1d, torch.nn.BatchNorm1d): fuse_conv_bn,
(torch.nn.Conv1d, torch.nn.BatchNorm1d, torch.nn.ReLU): fuse_conv_bn_relu,
(torch.nn.Conv2d, torch.nn.BatchNorm2d): fuse_conv_bn,
(torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.ReLU): fuse_conv_bn_relu,
(torch.nn.Conv3d, torch.nn.BatchNorm3d): fuse_conv_bn,
(torch.nn.Conv3d, torch.nn.BatchNorm3d, torch.nn.ReLU): fuse_conv_bn_relu,
(torch.nn.Conv1d, torch.nn.ReLU): torch.nn.intrinsic.ConvReLU1d,
(torch.nn.Conv2d, torch.nn.ReLU): torch.nn.intrinsic.ConvReLU2d,
(torch.nn.Conv3d, torch.nn.ReLU): torch.nn.intrinsic.ConvReLU3d,
(torch.nn.Linear, torch.nn.ReLU): torch.nn.intrinsic.LinearReLU,
(torch.nn.BatchNorm2d, torch.nn.ReLU): torch.nn.intrinsic.BNReLU2d,
(torch.nn.BatchNorm3d, torch.nn.ReLU): torch.nn.intrinsic.BNReLU3d,
}
types = tuple(type(m) for m in mod_list)
fuser_method = OP_LIST_TO_FUSER_METHOD.get(types, None)
if fuser_method is None:
raise NotImplementedError("Cannot fuse modules: {}".format(types))
new_mod = [None] * len(mod_list)
new_mod[0] = fuser_method(*mod_list)
for i in range(1, len(mod_list)):
new_mod[i] = torch.nn.Identity()
new_mod[i].training = mod_list[0].training
return new_mod
def _fuse_modules(model, modules_to_fuse, fuser_func=fuse_known_modules):
mod_list = []
for item in modules_to_fuse:
mod_list.append(_get_module(model, item))
# Fuse list of modules
new_mod_list = fuser_func(mod_list)
# Replace original module list with fused module list
for i, item in enumerate(modules_to_fuse):
_set_module(model, item, new_mod_list[i])
[docs]def fuse_modules(model, modules_to_fuse, inplace=False, fuser_func=fuse_known_modules):
r"""Fuses a list of modules into a single module
Fuses only the following sequence of modules:
conv, bn
conv, bn, relu
conv, relu
linear, relu
bn, relu
All other sequences are left unchanged.
For these sequences, replaces the first item in the list
with the fused module, replacing the rest of the modules
with identity.
Arguments:
model: Model containing the modules to be fused
modules_to_fuse: list of list of module names to fuse. Can also be a list
of strings if there is only a single list of modules to fuse.
inplace: bool specifying if fusion happens in place on the model, by default
a new model is returned
fuser_func: Function that takes in a list of modules and outputs a list of fused modules
of the same length. For example,
fuser_func([convModule, BNModule]) returns the list [ConvBNModule, nn.Identity()]
Defaults to torch.quantization.fuse_known_modules
Returns:
model with fused modules. A new copy is created if inplace=True.
Examples::
>>> m = myModel()
>>> # m is a module containing the sub-modules below
>>> modules_to_fuse = [ ['conv1', 'bn1', 'relu1'], ['submodule.conv', 'submodule.relu']]
>>> fused_m = torch.quantization.fuse_modules(m, modules_to_fuse)
>>> output = fused_m(input)
>>> m = myModel()
>>> # Alternately provide a single list of modules to fuse
>>> modules_to_fuse = ['conv1', 'bn1', 'relu1']
>>> fused_m = torch.quantization.fuse_modules(m, modules_to_fuse)
>>> output = fused_m(input)
"""
if not inplace:
model = copy.deepcopy(model)
if all(isinstance(module_element, str) for module_element in modules_to_fuse):
# Handle case of modules_to_fuse being a list
_fuse_modules(model, modules_to_fuse, fuser_func)
else:
# Handle case of modules_to_fuse being a list of lists
for module_list in modules_to_fuse:
_fuse_modules(model, module_list, fuser_func)
return model
