Skip to content

Model fp8 initialization sync #38

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

Merged
ananthsub merged 2 commits into from
Jun 13, 2025
Merged

Model fp8 initialization sync #38

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e17f10d
model fp8 initialization sync
ananthsub Jun 4, 2025
40fa865
right imports
ananthsub Jun 4, 2025
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
22 changes: 9 additions & 13 deletions src/megatron/hub/models/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
TorchFullyShardedDataParallel,
)
from megatron.core.enums import ModelType
from megatron.core.fp8_utils import is_float8tensor
from megatron.core.fp8_utils import correct_amax_history_if_needed
from megatron.core.transformer.module import Float16Module, MegatronModule

from megatron.hub.models.gpt import GPTConfig
Expand Down Expand Up @@ -178,18 +178,14 @@ def get_distributed_model(
# Fp16 conversion.
if model_config.fp16 or model_config.bf16:
model = [Float16Module(model_config, model_module) for model_module in model]
# The model_module.bfloat16()/model_module.half() above will call the inplace copy of TE's
# Float8Tensor, which will write an unwanted value (amax calculated from the current fp8
# param) to its amax_history. The following logic will correct the amax_history back.
for model_module in model:
for param in model_module.parameters():
if is_float8tensor(param) and param._fp8_meta is not None:
fp8_meta = param._fp8_meta["scaling_fwd"]
fp8_meta_index = param._fp8_meta_index
if hasattr(param, "get_high_precision_init_val"):
fp8_meta.amax_history[0][fp8_meta_index].copy_(param.get_high_precision_init_val().abs().max())
else:
fp8_meta.amax_history[0][fp8_meta_index] = 0

# Before TE2.x: The model_module.bfloat16()/model_module.half() above will call the inplace
# copy of TE's Float8Tensor, which will write an unwanted value (amax calculated
# from the current fp8 param) to its amax_history. The below function will correct
# the amax_history back.
# After TE2.x: Below function is an empty function and does nothing.
correct_amax_history_if_needed(model)

if wrap_with_ddp:
if use_torch_fsdp2:
DP = TorchFullyShardedDataParallel
Expand Down