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Disable integration test between optimizer-in-backward and pp #793

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Disable integration test between optimizer-in-backward and pp #793

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5fa1c4a
disable integration test between optimizer-in-backward with pp, add t…
mori360 Jan 16, 2025
84488f6
change NotImplementedError message
mori360 Jan 16, 2025
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16 changes: 12 additions & 4 deletions tests/integration_tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -378,14 +378,22 @@ def build_test_list():
[
[
"--checkpoint.enable_checkpoint",
"--experimental.pipeline_parallel_degree 2",
"--training.tensor_parallel_degree=2",
"--experimental.context_parallel_degree=2",
"--training.enable_cpu_offload",
"--optimizer.early_step_in_backward",
],
[
"--training.tensor_parallel_degree=2",
"--experimental.context_parallel_degree=2",
"--training.data_parallel_replicate_degree=2",
"--training.enable_cpu_offload",
"--optimizer.early_step_in_backward",
],
],
"Enable CPU Offload with PP",
"enable_cpu_offload+PP",
ngpu=4,
"Enable CPU Offload, Optimizer in backward with TP, DP, CP",
"cpu_offload+opt_in_bwd+TP+DP+CP",
ngpu=8,
),
OverrideDefinitions(
[
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33 changes: 22 additions & 11 deletions torchtitan/optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,30 +81,37 @@ def __init__(
) -> None:
self.optimizers = []
self.model_parts = model_parts
optim_dict = {}
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@mori360 mori360 Jan 16, 2025 •
edited
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collect all optims in optim_dict, avoid bugs with only valid hooks at the last self.model_parts if len(self.model_parts)>1 (for future support of multi schedule pp)

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The fix for optim dict lgtm

for model in self.model_parts:
if name == "Adam":
# TODO: make the optimizer options configurable by toml/cmd args
optim_dict = {
param: torch.optim.Adam([param], **optimizer_kwargs)
for param in model.parameters()
}
optim_dict.update(
{
param: torch.optim.Adam([param], **optimizer_kwargs)
for param in model.parameters()
}
)
elif name == "AdamW":
optim_dict = {
param: torch.optim.AdamW([param], **optimizer_kwargs)
for param in model.parameters()
}
optim_dict.update(
{
param: torch.optim.AdamW([param], **optimizer_kwargs)
for param in model.parameters()
}
)
else:
raise NotImplementedError(f"Optimizer {name} not added.")

def optim_hook(param) -> None:
optim_dict[param].step()
optim_dict[param].zero_grad()
def optim_hook(param) -> None:
optim_dict[param].step()
optim_dict[param].zero_grad()

for model in self.model_parts:
for param in model.parameters():
if param.requires_grad:
param.register_post_accumulate_grad_hook(optim_hook)

self.optimizers.extend([optim_dict[param] for param in model.parameters()])

self._validate_length(
sum(
len([param for param in model.parameters()])
Expand All @@ -127,6 +134,10 @@ def build_optimizers(
step() and zero_grad() method for all the child optimizers.
"""
optim_in_bwd = job_config.optimizer.early_step_in_backward
if optim_in_bwd and job_config.experimental.pipeline_parallel_degree > 1:
raise NotImplementedError(
"OptimizersInBackwardContainer is not supported with pipeline parallelism"
)
name = job_config.optimizer.name
lr = job_config.optimizer.lr
fused = job_config.optimizer.fused
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