Skip to content

fix bug of ut weight convert #1170

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

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

fix bug of ut weight convert #1170

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
11 changes: 6 additions & 5 deletions tests/modeling_test_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
from ml_dtypes import bfloat16

import mindspore as ms
from mindspore import mint, nn, ops
from mindspore import nn, ops

logger = logging.getLogger("ModelingsUnitTest")

Expand Down Expand Up @@ -131,10 +131,6 @@ def get_pt2ms_mappings(m):
mappings[f"{name}.running_mean"] = f"{name}.moving_mean", lambda x: x
mappings[f"{name}.running_var"] = f"{name}.moving_variance", lambda x: x
mappings[f"{name}.num_batches_tracked"] = None, lambda x: x
elif isinstance(cell, (mint.nn.BatchNorm1d, mint.nn.BatchNorm2d, mint.nn.BatchNorm3d)):
# TODO: for mint.nn, the dtype for each param should expected to be same among torch and mindspore
# this is a temporary fix, delete this branch in future.
mappings[f"{name}.num_batches_tracked"] = f"{name}.num_batches_tracked", lambda x: x.to(ms.float32)
return mappings


Expand All @@ -150,6 +146,11 @@ def convert_state_dict(m, state_dict_pt):
state_dict_ms = {}
for name_pt, data_pt in state_dict_pt.items():
name_ms, data_mapping = mappings.get(name_pt, (name_pt, lambda x: x))
# for torch back compatibility
# for torch <2.0, dtype of num_batches_tracked is int32, for torch>=2.0, dtype of num_batches_tracked is int64,
# mindspore.mint is aligned with torch>=2.0
if "num_batches_tracked" in name_pt and data_pt.dtype == torch.int32:
data_pt = data_pt.to(torch.int64)
data_ms = ms.Parameter(
data_mapping(ms.Tensor.from_numpy(data_pt.float().numpy()).to(dtype_mappings[data_pt.dtype])), name=name_ms
)
Expand Down