Skip to content

Feat/Decoder migration: DeepSeek/Gemma3/Llama4 #3114

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

Draft
hsuan-lun-chiang wants to merge 1 commit into
base: main
Choose a base branch
from
Draft

Feat/Decoder migration: DeepSeek/Gemma3/Llama4 #3114

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
6 changes: 3 additions & 3 deletions src/maxtext/configs/base.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1126,9 +1126,9 @@ position_id_per_seconds: 25
subslice_shape: ""

# NNX
enable_nnx: False
pure_nnx_decoder: False
pure_nnx: False
enable_nnx: false
pure_nnx_decoder: false
pure_nnx: false

################################## Qwen3-Next Specific Configs ##################################
# Kernel size for the 1D convolution in the Gated Delta Net
Expand Down
10 changes: 10 additions & 0 deletions src/maxtext/layers/initializers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,16 @@ def variable_to_logically_partitioned(variable: nnx.VariableState):
out_sharding = metadata["sharding"]

if out_sharding is not None:
if nnx.PARTITION_NAME in metadata:
partition_name = metadata[nnx.PARTITION_NAME]
scan_axis = metadata.get("param_scan_axis", 0) if variable.type == nnx.Param else 0

sharding_list = [out_sharding] if isinstance(out_sharding, str) else list(out_sharding)
if partition_name not in sharding_list:
sharding_list.insert(scan_axis, partition_name)

out_sharding = tuple(sharding_list)

return nn.LogicallyPartitioned( # type: ignore[wrong-keyword-args]
variable.value,
out_sharding, # type: ignore[arg-type]
Expand Down
Loading
Loading