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[auto-parallel] add auto-offload feature (#3154)

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* add auto-offload feature

* polish code

* fix syn offload runtime pass bug

* add offload example

* fix offload testing bug

* fix example testing bug
This commit is contained in:
Zihao
2023-03-21 14:17:41 +08:00
committed by GitHub
parent 258b43317c
commit 18dbe76cae
18 changed files with 2833 additions and 0 deletions
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colossalai/auto_parallel/offload/runtime.py Normal file
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from typing import List
import torch
from torch.fx.node import Node
from .region import Region
from .util import GlobalRuntimeInfo, requires_upload_p_in_fwd
class SynPreFwdPostBwdOP(torch.autograd.Function):
"""
A customized prefetch and offload operation.
Args:
input_: input tensor.
fwd_info: information dict, which contains region indices
that need to be uploaded or freed during forward pass.
bwd_info: information dict, which contains region indices
that need to be uploaded during backward pass.
"""
@staticmethod
def forward(ctx, input_, fwd_info, bwd_info):
ctx.bwd_info = bwd_info
d2h_rid = fwd_info.get('d2h_rid', None)
if d2h_rid is not None:
free_region = GlobalRuntimeInfo.region_list[d2h_rid]
assert isinstance(free_region, Region)
free_region.free_cuda_data()
h2d_rid = fwd_info.get('h2d_rid', None)
if h2d_rid is not None:
h2d_region = GlobalRuntimeInfo.region_list[h2d_rid]
assert isinstance(h2d_region, Region)
h2d_region.move_param_to_cuda()
return input_
@staticmethod
def backward(ctx, grad_output):
h2d_rid = ctx.bwd_info.get('h2d_rid', None)
if h2d_rid is not None:
pref_region = GlobalRuntimeInfo.region_list[h2d_rid]
assert isinstance(pref_region, Region)
pref_region.move_param_to_cuda()
return grad_output, None, None
class AsynPreFwdPostBwdOP(torch.autograd.Function):
"""
A customized prefetch and offload operation.
Args:
input_: input tensor.
fwd_info: information dict, which contains region indices
that need to be prefetched, waited, or freed during forward pass.
bwd_info: information dict, which contains region indices
that need to be prefetched or waited during backward pass.
"""
@staticmethod
def forward(ctx, input_, fwd_info, bwd_info):
ctx.bwd_info = bwd_info
sync_rid = fwd_info.get('sync_rid', None)
if sync_rid is not None:
prefetch_event = GlobalRuntimeInfo.fwd_prefetch_event_map.get(
sync_rid, None)
if prefetch_event:
prefetch_event.wait()
h2d_rid = fwd_info.get('h2d_rid', None)
if h2d_rid is not None:
pref_region = GlobalRuntimeInfo.region_list[h2d_rid]
assert isinstance(pref_region, Region)
master_stream = torch.cuda.current_stream()
with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream):
GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream)
pref_region.move_param_to_cuda()
prefetch_event = torch.cuda.Event()
prefetch_event.record(GlobalRuntimeInfo.h2d_stream)
GlobalRuntimeInfo.fwd_prefetch_event_map[h2d_rid] = prefetch_event
return input_
@staticmethod
def backward(ctx, grad_output):
sync_rid = ctx.bwd_info.get('sync_rid', None)
if sync_rid is not None:
wait_region = GlobalRuntimeInfo.region_list[sync_rid]
assert isinstance(wait_region, Region)
prefetch_event = GlobalRuntimeInfo.bwd_prefetch_event_map.get(
sync_rid, None)
if prefetch_event:
prefetch_event.wait()
else:
wait_region.move_param_to_cuda()
h2d_rid = ctx.bwd_info.get('h2d_rid', None)
if h2d_rid is not None:
pref_region = GlobalRuntimeInfo.region_list[h2d_rid]
assert isinstance(pref_region, Region)
master_stream = torch.cuda.current_stream()
with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream):
GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream)
pref_region.move_param_to_cuda()
prefetch_event = torch.cuda.Event()
prefetch_event.record(GlobalRuntimeInfo.h2d_stream)
GlobalRuntimeInfo.bwd_prefetch_event_map[h2d_rid] = prefetch_event
return grad_output, None, None
def convert_fwd_upload_bwd_offload_to_action(tensor, fwd_info, bwd_info):
'''
Convert Upload and Offload operation into runtime action.
Argument:
tensor(torch.Tensor): input tensor.
fwd_info(dict): information dict, which contains region indices
that need to be uploaded, or freed during forward pass.
bwd_info(dict): information dict, which contains region indices
that need to be uploaded during backward pass.
'''
with torch._C.DisableTorchFunction():
ret = SynPreFwdPostBwdOP.apply(tensor, fwd_info, bwd_info)
return ret
def convert_fwd_prefetch_bwd_offload_to_action(tensor, fwd_info, bwd_info):
'''
Convert Prefetch and Offload operation into runtime action.
Argument:
tensor(torch.Tensor): input tensor.
fwd_info(dict): information dict, which contains region indices
that need to be prefetched, waited, or freed during forward pass.
bwd_info(dict): information dict, which contains region indices
that need to be prefetched or waited during backward pass.
'''
with torch._C.DisableTorchFunction():
ret = AsynPreFwdPostBwdOP.apply(tensor, fwd_info, bwd_info)
return ret
def replace_node_users(orig_node: Node, inserted_node: Node, rep_user_nodes: List[Node] = None):
user_list = list(orig_node.users.keys())
if rep_user_nodes is not None:
user_list = rep_user_nodes
for user in user_list:
if user == inserted_node:
continue
new_args = list(user.args)
new_kwargs = dict(user.kwargs)
# the origin node may be a positional argument or key word argument of user node
if orig_node in new_args:
# substitute the origin node with offload_apply_node
new_args[new_args.index(orig_node)] = inserted_node
user.args = tuple(new_args)
elif str(orig_node) in new_kwargs:
# substitute the origin node with offload_apply_node
new_kwargs[str(orig_node)] = inserted_node
user.kwargs = new_kwargs
def runtime_syn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[Region]):
"""
This pass is used to add the synchronous upload and offload spec apply node to the origin graph.
"""
mod_graph = gm.graph
last_inp_node = tuple(mod_graph.nodes)[0]
for r_idx, region in enumerate(region_list):
# forward upload
fwd_info = {}
if requires_upload_p_in_fwd(region_list[region.shared_rid]):
fwd_info['h2d_rid'] = region.r_id
# forward offload
if r_idx > 0 and region_list[r_idx - 1].need_offload:
fwd_info['d2h_rid'] = r_idx - 1
bwd_info = {}
# backward upload
if r_idx > 0 and region_list[r_idx - 1].need_offload:
bwd_info['h2d_rid'] = region_list[r_idx - 1].r_id
if fwd_info or bwd_info:
with mod_graph.inserting_after(last_inp_node):
new_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action,
args=(last_inp_node, fwd_info, bwd_info))
replace_node_users(last_inp_node, new_node)
last_inp_node = region.nodes[-1]
return gm
def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[Region]):
"""
This pass is used to add the asynchronous prefetch and offload spec apply node to the origin graph.
"""
mod_graph = gm.graph
# upload parameters of the first region
last_inp_node = tuple(mod_graph.nodes)[0]
first_region_with_p = [
region for region in region_list if region.param_size][0]
fwd_info = {"h2d_rid": first_region_with_p.r_id}
with mod_graph.inserting_after(last_inp_node):
upload_apply_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action,
args=(last_inp_node, fwd_info, {}))
replace_node_users(last_inp_node, upload_apply_node)
last_inp_node = upload_apply_node
for r_idx, region in enumerate(region_list):
# forward prefetch
fwd_info = {}
if region.param_size:
fwd_info['sync_rid'] = region.r_id
fwd_prefetch_region = region.fwd_prefetch_region
if fwd_prefetch_region and requires_upload_p_in_fwd(region_list[fwd_prefetch_region.shared_rid]):
fwd_info['h2d_rid'] = fwd_prefetch_region.r_id
# forward offload
if r_idx > 0 and region_list[r_idx-1].need_offload:
fwd_info['d2h_rid'] = r_idx - 1
bwd_info = {}
# backward prefetch
if r_idx > 0 and region_list[r_idx-1].need_offload:
bwd_info['sync_rid'] = r_idx - 1
if r_idx > 0 and region_list[r_idx-1].bwd_prefetch_region:
bwd_info['h2d_rid'] = region_list[r_idx-1].bwd_prefetch_region.r_id
if fwd_info or bwd_info:
with mod_graph.inserting_after(last_inp_node):
new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action,
args=(last_inp_node, fwd_info, bwd_info))
replace_node_users(last_inp_node, new_node)
last_inp_node = region.nodes[-1]
if region.bwd_prefetch_region:
bwd_info = {'h2d_rid': region.bwd_prefetch_region.r_id}
with mod_graph.inserting_after(last_inp_node):
new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action,
args=(last_inp_node, {}, bwd_info))
replace_node_users(last_inp_node, new_node)
# gm.graph.print_tabular()
return gm