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Integration with DCP #978

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Integration with DCP #978

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45 changes: 45 additions & 0 deletions test/test_transformer.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
# Copyright (c) Meta Platforms, Inc. and affiliates
import torch
from pippy import annotate_split_points, Pipe, SplitPoint
import torch.distributed.checkpoint as dcp
import tempfile


d_hid = 16
Expand Down Expand Up @@ -66,6 +68,49 @@ def get_layers(module):
return layers


def pipe_to_sd(pipe):
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@LucasLLC LucasLLC Mar 18, 2024

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@wz337 , might be interesting in dist state dict

sd = {}
for stage_idx in range(pipe.num_stages):
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@wconstab wconstab Mar 21, 2024

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something a little fishy about this proposal (equally so for both option 1 and 2) is that it's not likely you'd want to iterate all the stages in the pipe and load/save them.

Example 1: simple pipeline with 4 gpus
rank0: save/load pipe.submod_0 only
...
Example 2: complex pipeline with 4 gpus, 2 stages per gpu
rank0: save/load pipe.submod_0 and pipe.submod_4
rank1: save/load pipe.submod_1 and pipe.submod_5
...

stage_mod = pipe.get_stage_module(stage_idx)
sd[f"stage_{stage_idx}"] = stage_mod
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@wconstab wconstab Mar 21, 2024 •
edited
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not really clear to me why we need to add a prefix at all.

orig model
-----------
Transformer
  embedding
  layers
    0 
    1

split model
-----------
  submod0
     embedding
     layers
        0

  submod 1
    layers
      1

There should be no duplication of fqns between submods/stages.

what are we doing about the 'submod_0' part in the fqn? when we do stage_mod = pipe.get_stage_module(stage_idx) does that return us a module that has top level keys like embedding and layers or a module that has a top level key of submod_n?

If the former, can't we just save/load the keys as usual?

If the latter, we can still save/load without a prefix of stage_{idx} i think, but we'll sadly be uncompatible to load into a non-PP model later on if we want to.

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@kwen2501 kwen2501 Mar 27, 2024

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Former. @wconstab

return sd

with tempfile.TemporaryDirectory() as tmpdir:
#Simulate saving the pipe
# Option 1:
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@kwen2501 kwen2501 Mar 19, 2024

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I think Option 1 would be more likely used than Option 2 in realistic setting. Could you please uncomment this block of code?

# for stage_idx in range(pipe.num_stages):
# print(f"Saving pipeline stage {stage_idx}")
# stage_mod = pipe.get_stage_module(stage_idx)
# dcp.save(
# {f"stage_{stage_idx}": stage_mod},
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@kwen2501 kwen2501 Mar 19, 2024

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Curious, is the dict required by API of DCP? Can a user directly save stage_mod?

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@wconstab wconstab Mar 21, 2024

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why does this matter? i think the DCP api had reasons for interfacing with dict instead of model, adding a new variant that takes model and gets its dict should be possible, but i think it's clearer this way that the only part of the model that gets saved is the dict

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@kwen2501 kwen2501 Mar 27, 2024

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Just to be clear: I like saving the state dict too (instead of the module). That's more composable to me.
My question above is: is {f"stage_{stage_idx}": stage_mod} necessary?

# checkpoint_id=f"{tmpdir}_{stage_idx}"
# )
# Option 2:
sd = pipe_to_sd(pipe)
dcp.save(state_dict, checkpoint_id=tmpdir)


#Simulate loading the pipe
# Option 1:
# for stage_idx in range(pipe.num_stages):
# print(f"Loading pipeline stage {stage_idx}")
# stage_mod = pipe.get_stage_module(stage_idx)
# dcp.load(
# {f"stage_{stage_idx}": stage_mod},
# checkpoint_id=f"{tmpdir}_{stage_idx}"
# )

#Option 2:
new_pipe = Pipe.from_tracing(
transformer,
1,
(x,),
)
sd = pipe_to_sd(new_pipe)
dcp.load(sd, checkpoint_id=tmpdir)

pipe = new_pipe

# Collect all layers in pipe
layers = []
for stage_idx in range(pipe.num_stages):
Expand Down