Correctly create tied key mapping in post_init, and dynamic tie weight

src/transformers/models/esm/modeling_esm.py

@@ -732,8 +732,6 @@ def __init__(self, config):
         self.esm = EsmModel(config, add_pooling_layer=False)
         self.lm_head = EsmLMHead(config)
 
-        self.init_weights()
-
         self.post_init()
 
     def get_output_embeddings(self):
@@ -828,8 +826,6 @@ def __init__(self, config):
         self.esm = EsmModel(config, add_pooling_layer=False)
         self.classifier = EsmClassificationHead(config)
 
-        self.init_weights()
-
         self.post_init()
 
     @can_return_tuple
@@ -903,8 +899,6 @@ def __init__(self, config):
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
         self.classifier = nn.Linear(config.hidden_size, config.num_labels)
 
-        self.init_weights()
-
         self.post_init()
 
     @can_return_tuple

src/transformers/models/hubert/modeling_hubert.py

@@ -993,7 +993,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

src/transformers/models/idefics/modeling_idefics.py

@@ -1111,7 +1111,7 @@ def __init__(self, config, vision_model=None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of
         IdeficsDecoupledLinear and IdeficsDecoupledEmbedding.

src/transformers/models/sew/modeling_sew.py

@@ -857,7 +857,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

src/transformers/models/sew_d/modeling_sew_d.py

@@ -1400,7 +1400,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

src/transformers/models/unispeech/modeling_unispeech.py

@@ -1210,7 +1210,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -1206,7 +1206,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1684,7 +1684,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

src/transformers/models/wavlm/modeling_wavlm.py

@@ -1135,7 +1135,7 @@ def __init__(self, config, target_lang: Optional[str] = None):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def tie_weights(self, missing_keys=None):
+    def tie_weights(self, **kwargs):
         """
         This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
         passing `target_lang=...` to `from_pretrained(...)`.

tests/models/openai/test_modeling_openai.py

@@ -269,6 +269,13 @@ def test_model_from_pretrained(self):
         model = OpenAIGPTModel.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
+    @unittest.skip("Tied weights mapping is reversed, so this is supposed to error out")
+    def test_correct_missing_keys(self):
+        # openai defines `_tied_weights_keys = {"transformer.tokens_embed.weight": "lm_head.weight"}` instead
+        # of the usual `_tied_weights_keys = {"lm_head.weight": "transformer.tokens_embed.weight"}`, so removing
+        # the head parameters actually removes the source weight, so this test is supposed to fail
+        pass
+
 
 @require_torch
 class OPENAIGPTModelLanguageGenerationTest(unittest.TestCase):

tests/utils/test_modeling_utils.py

@@ -180,7 +180,7 @@ def __init__(self, config):
         def forward(self, x):
             return self.linear_2(self.linear(x))
 
-        def tie_weights(self, missing_keys=None):
+        def tie_weights(self, missing_keys=None, **kwargs):
             self.linear_2.weight = self.linear.weight
             if missing_keys is not None:
                 missing_keys.discard("linear_2.weight")
@@ -254,7 +254,7 @@ def __init__(self, config):
         def forward(self, x):
             return self.decoder(self.base(x))
 
-        def tie_weights(self, missing_keys=None):
+        def tie_weights(self, missing_keys=None, **kwargs):
             self.decoder.weight = self.base.linear.weight
             if missing_keys is not None:
                 missing_keys.discard("decoder.weight")