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Fix Distributed Checkpoint Tutorial #3068

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Merged
merged 5 commits into from
Oct 15, 2024
Merged

Fix Distributed Checkpoint Tutorial #3068

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d490aef
Fix undefined model and optimizer variables (#3067)
LTMeyer Sep 30, 2024
0e52e07
Fix missing import of Stateful (#3067)
LTMeyer Sep 30, 2024
1f1aad6
Fix state_dict passed to dcp.load (#3067)
LTMeyer Sep 30, 2024
bdd9e4a
Merge branch 'main' into fix-dcp-checkpoint-tutorial
LTMeyer Oct 15, 2024
219e199
Merge branch 'main' into fix-dcp-checkpoint-tutorial
svekars Oct 15, 2024
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12 changes: 3 additions & 9 deletions recipes_source/distributed_checkpoint_recipe.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -82,7 +82,7 @@ Now, let's create a toy module, wrap it with FSDP, feed it with some dummy input

def state_dict(self):
# this line automatically manages FSDP FQN's, as well as sets the default state dict type to FSDP.SHARDED_STATE_DICT
model_state_dict, optimizer_state_dict = get_state_dict(model, optimizer)
model_state_dict, optimizer_state_dict = get_state_dict(self.model, self.optimizer)
return {
"model": model_state_dict,
"optim": optimizer_state_dict
Expand Down Expand Up @@ -178,6 +178,7 @@ The reason that we need the ``state_dict`` prior to loading is:
import torch
import torch.distributed as dist
import torch.distributed.checkpoint as dcp
from torch.distributed.checkpoint.stateful import Stateful
from torch.distributed.checkpoint.state_dict import get_state_dict, set_state_dict
import torch.multiprocessing as mp
import torch.nn as nn
Expand All @@ -202,7 +203,7 @@ The reason that we need the ``state_dict`` prior to loading is:

def state_dict(self):
# this line automatically manages FSDP FQN's, as well as sets the default state dict type to FSDP.SHARDED_STATE_DICT
model_state_dict, optimizer_state_dict = get_state_dict(model, optimizer)
model_state_dict, optimizer_state_dict = get_state_dict(self.model, self.optimizer)
return {
"model": model_state_dict,
"optim": optimizer_state_dict
Expand Down Expand Up @@ -252,13 +253,6 @@ The reason that we need the ``state_dict`` prior to loading is:
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

state_dict = { "app": AppState(model, optimizer)}
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
# generates the state dict we will load into
model_state_dict, optimizer_state_dict = get_state_dict(model, optimizer)
state_dict = {
"model": model_state_dict,
"optimizer": optimizer_state_dict
}
dcp.load(
state_dict=state_dict,
checkpoint_id=CHECKPOINT_DIR,
Expand Down
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