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sampler.py: Add args to specify params to optimize over + not copy full model. #102

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sampler.py: Add args to specify params to optimize over + not copy full model. #102

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0b78aee
sample.py only copy params that are optimized over
wiwu2390 Mar 9, 2025
91aba28
Copy full model when cores > 1
wiwu2390 Mar 9, 2025
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22 changes: 18 additions & 4 deletions src/devinterp/backends/default/slt/sampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@ def sample_single_chain(
optimize_over_per_model_param: Optional[dict] = None,
callbacks: List[SamplerCallback] = [],
use_amp: bool = False,
copy_full_model: bool=True,
**kwargs,
):
if grad_accum_steps > 1:
Expand All @@ -53,8 +54,16 @@ def sample_single_chain(
"You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)"
)

# Initialize new model and optimizer for this chain
model = deepcopy(ref_model).to(device)
if copy_full_model:
model = deepcopy(ref_model).to(device)
else:
model = ref_model.to(device)
if 'named_params' in optimizer_kwargs:
named_params = optimizer_kwargs.pop('named_params')
else:
named_params = model.named_parameters()
if not copy_full_model:
ref_named_params = deepcopy(named_params)

if "temperature" in optimizer_kwargs:
assert (
Expand All @@ -78,7 +87,7 @@ def sample_single_chain(

if optimize_over_per_model_param:
param_groups = []
for name, parameter in model.named_parameters():
for name, parameter in named_params:
param_groups.append(
{
"params": parameter,
Expand All @@ -90,7 +99,7 @@ def sample_single_chain(
**optimizer_kwargs,
)
else:
optimizer = sampling_method(model.parameters(), **optimizer_kwargs)
optimizer = sampling_method(named_params.values(), **optimizer_kwargs)

if seed is not None:
torch.manual_seed(seed)
Expand Down Expand Up @@ -138,6 +147,10 @@ def sample_single_chain(
cumulative_loss = 0
pbar.update(1)

# Restore orginal params
if not copy_full_model:
model.load_state_dict(ref_named_params, strict=False)


def _sample_single_chain(kwargs):
pickled_args = ["evaluate", "loader"]
Expand Down Expand Up @@ -342,6 +355,7 @@ def evaluate(model, data):
verbose=verbose,
optimize_over_per_model_param=optimize_over_per_model_param,
use_amp=use_amp,
copy_full_model=cores > 1,
)

if cores > 1:
Expand Down