From b345f4e7f7f852f7def0ebe473d21419ae235fdc Mon Sep 17 00:00:00 2001
From: Arthur Zucker <arthur.zucker@gmail.com>
Date: Tue, 26 Mar 2024 20:15:07 +0900
Subject: [PATCH] warning was in info mode.
---
src/transformers/modeling_utils.py | 14 ++++++--------
1 file changed, 6 insertions(+), 8 deletions(-)
diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index 2f24170ee5..6b4f780a59 100644
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -595,12 +595,12 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix):
state_dict._metadata = metadata
error_msgs = []
+ unexpected_keys, missing_keys = [], []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module: nn.Module, state_dict, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
- unexpected_keys, missing_keys = [], []
args = (state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
# Parameters of module and children will start with prefix. We can exit early if there are none in this
# state_dict
@@ -631,7 +631,7 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix):
# it's safe to delete it.
del state_dict
- return error_msgs
+ return error_msgs, unexpected_keys, missing_keys
def find_submodule_and_param_name(model, long_key, start_prefix):
@@ -3902,7 +3902,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
remove_prefix_from_model,
ignore_mismatched_sizes,
)
- error_msgs = _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
+ error_msgs, unexpected_keys, missing_keys = _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
offload_index = None
else:
# Sharded checkpoint or whole but low_cpu_mem_usage==True
@@ -3975,8 +3975,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
)
error_msgs += new_error_msgs
else:
- error_msgs += _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
-
+ mmsg, unexpected_keys, missing_keys = _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
+ error_msgs += mmsg
# force memory release
del state_dict
gc.collect()
@@ -4010,9 +4010,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
if len(unexpected_keys) > 0:
- archs = [] if model.config.architectures is None else model.config.architectures
- warner = logger.warning if model.__class__.__name__ in archs else logger.info
- warner(
+ logger.warning(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"
--
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