Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Fix Non-Contiguous Tensor Issue in Checkpoint Consolidation #708

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
Open

Fix Non-Contiguous Tensor Issue in Checkpoint Consolidation #708

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
ece4f98
Fix non-contiguous tensor issue in checkpoint consolidation
Oct 5, 2024
5965190
Update PR with more edge cases where tensor may not be contiguous aft…
Oct 7, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 6 additions & 4 deletions optimum/neuron/distributed/checkpointing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -145,15 +145,17 @@ def consolidate_tensor_parallel_checkpoints(
# This might not be the case anymore when `ParameterMetadata` uses slices.
sharded_metadata = sharded_metadatas[name]
if sharded_metadata.is_tied:
consolidated_state_dict[original_name] = state_dicts[0][name].to("cpu")
consolidated_state_dict[original_name] = state_dicts[0][name].to("cpu").contiguous()
else:
weights = [state_dict[name] for state_dict in state_dicts]
# Ensure that all tensors are contiguous before concatenating or further processing
weights = [state_dict[name].contiguous() for state_dict in state_dicts]
tp_size = len(weights)

full_weight = torch.cat(
weights,
dim=sharded_metadata.partition_dim,
)
full_weight = full_weight.to("cpu")
).to("cpu").contiguous() # Ensure the result is also contiguous

if weight_name in ["weight_k", "weight_v", "bias_k", "bias_v"]:
full_weight = (
torch.chunk(full_weight, gqa_qkv_metadata["kv_size_multiplier"], dim=0)[0].detach().clone()
Expand Down
Loading