blob: d6ecac659cf111a3d2a3c93d7f9788550e24e86b [file] [log] [blame]
import torch.cuda.comm as comm
from torch.cuda._utils import _get_device_index
def _is_script_module(module):
import torch.jit
return isinstance(module, torch.jit.ScriptModule)
def _is_script_method(module):
import torch.jit
return isinstance(module, torch._C.ScriptMethod)
def _init_script_module():
import torch.jit
return torch.jit.ScriptModule()
def _is_jit_enabled():
import torch.jit
return torch.jit._enabled
# Check if we can safely replicate the module.
# there are three types of module:
# 1. python modules
# 2. weak python modules (nn.Module annotated by @weak_module)
# 3. ScriptModule
#
# currently a module cannot be replicated properly if the descendants of
# any ScriptModule contains python module (type 1 above)
def _replicatable_module(module, memo=None):
# module.modules() contains module itself as the first element
def descendant_modules(module):
gen = module.modules()
next(gen)
return gen
if not _is_jit_enabled():
return True
if memo is None:
memo = set()
# memorize visited modules
memo.add(module)
if _is_script_module(module):
memo.update(descendant_modules(module))
return all(_is_script_module(descendant) for
descendant in descendant_modules(module))
for child in module.children():
# since any unreplicatable module will cause the check to return
# False early, visited modules here can be safely ignored.
if child in memo:
continue
if not _replicatable_module(child, memo):
return False
return True
def _copy_scriptmodule_methods(modules, module_copies, module_indices):
for i, module in enumerate(modules):
if not _is_script_module(module):
continue
replica = module_copies[i]
for method_name in module._c._method_names():
replica._c.clone_method(module._c, method_name)
def _broadcast_coalesced_reshape(tensors, devices, detach=False):
from ._functions import Broadcast
if detach:
return comm.broadcast_coalesced(tensors, devices)
else:
# Use the autograd function to broadcast if not detach
if len(tensors) > 0:
tensor_copies = Broadcast.apply(devices, *tensors)
return [tensor_copies[i:i + len(tensors)]
for i in range(0, len(tensor_copies), len(tensors))]
else:
return []
def replicate(network, devices, detach=False):
if not _replicatable_module(network):
raise RuntimeError("Cannot replicate network where python modules are "
"childrens of ScriptModule")
devices = list(map(lambda x: _get_device_index(x, True), devices))
num_replicas = len(devices)
params = list(network.parameters())
param_indices = {param: idx for idx, param in enumerate(params)}
param_copies = _broadcast_coalesced_reshape(params, devices, detach)
buffers = list(network.buffers())
buffers_rg = []
buffers_not_rg = []
for buf in buffers:
if buf.requires_grad and not detach:
buffers_rg.append(buf)
else:
buffers_not_rg.append(buf)
buffer_indices_rg = {buf: idx for idx, buf in enumerate(buffers_rg)}
buffer_indices_not_rg = {buf: idx for idx, buf in enumerate(buffers_not_rg)}
buffer_copies_rg = _broadcast_coalesced_reshape(buffers_rg, devices, detach=detach)
buffer_copies_not_rg = _broadcast_coalesced_reshape(buffers_not_rg, devices, detach=True)
modules = list(network.modules())
module_copies = [[] for device in devices]
module_indices = {}
scriptmodule_skip_attr = {"_parameters", "_buffers", "_modules", "forward", "_c"}
for i, module in enumerate(modules):
module_indices[module] = i
for j in range(num_replicas):
if _is_script_module(module):
# we have to initialize ScriptModule properly so that
# it works with pybind11
replica = _init_script_module()
attribute_names = set(entry[0] for entry in module._c._get_attributes())
keys = set(module.__dict__.keys()) - scriptmodule_skip_attr - attribute_names
for key in keys:
if not _is_script_method(module.__dict__[key]):
replica.__dict__[key] = module.__dict__[key]
for name, the_type, value in module._c._get_attributes():
if name in module._buffers.keys():
continue
replica._c._register_attribute(name, the_type, value)
else:
replica = module.__new__(type(module))
replica.__dict__ = module.__dict__.copy()
replica._parameters = replica._parameters.copy()
replica._buffers = replica._buffers.copy()
replica._modules = replica._modules.copy()
module_copies[j].append(replica)
for i, module in enumerate(modules):
for key, child in module._modules.items():
if child is None:
for j in range(num_replicas):
replica = module_copies[j][i]
replica._modules[key] = None
else:
module_idx = module_indices[child]
for j in range(num_replicas):
replica = module_copies[j][i]
replica._modules[key] = module_copies[j][module_idx]
for key, param in module._parameters.items():
if param is None:
for j in range(num_replicas):
replica = module_copies[j][i]
replica._parameters[key] = None
else:
param_idx = param_indices[param]
for j in range(num_replicas):
replica = module_copies[j][i]
replica._parameters[key] = param_copies[j][param_idx]
for key, buf in module._buffers.items():
if buf is None:
for j in range(num_replicas):
replica = module_copies[j][i]
replica._buffers[key] = None
else:
if buf.requires_grad and not detach:
buffer_copies = buffer_copies_rg
buffer_idx = buffer_indices_rg[buf]
else:
buffer_copies = buffer_copies_not_rg
buffer_idx = buffer_indices_not_rg[buf]
for j in range(num_replicas):
replica = module_copies[j][i]
replica._buffers[key] = buffer_copies[j][buffer_idx]
for j in range(num_replicas):
_copy_scriptmodule_methods(modules, module_copies[j], module_indices)
return [module_copies[j][0] for j in range(num_replicas)]