removed gemma3 eager warning

src/transformers/models/gemma2/modeling_gemma2.py

@@ -534,11 +534,6 @@ def forward(
         "What is your favorite condiment?"
         ```"""
 
-        if self.training and self.config._attn_implementation != "eager":
-            logger.warning_once(
-                "It is strongly recommended to train Gemma2 models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

src/transformers/models/gemma2/modular_gemma2.py

@@ -520,11 +520,6 @@ def forward(
         "What is your favorite condiment?"
         ```"""
 
-        if self.training and self.config._attn_implementation != "eager":
-            logger.warning_once(
-                "It is strongly recommended to train Gemma2 models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

src/transformers/models/gemma3/modeling_gemma3.py

@@ -654,11 +654,6 @@ def forward(
         "What is your favorite condiment?"
         ```"""
 
-        if self.training and self.config._attn_implementation != "eager":
-            logger.warning_once(
-                "It is strongly recommended to train Gemma3 models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

src/transformers/models/gemma3n/modeling_gemma3n.py

@@ -1808,11 +1808,6 @@ def forward(
         "What is your favorite condiment?"
         ```"""
 
-        if self.training and self.config._attn_implementation != "eager":
-            logger.warning_once(
-                "It is strongly recommended to train Gemma3n models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

src/transformers/models/t5gemma/modeling_t5gemma.py

@@ -41,7 +41,7 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
 from ...utils.deprecation import deprecate_kwarg
 from ...utils.generic import OutputRecorder, check_model_inputs
 from .configuration_t5gemma import T5GemmaConfig, T5GemmaModuleConfig
@@ -1064,15 +1064,6 @@ def forward(
             config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
             (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
         """
-        if self.training and self.config._attn_implementation != "eager":
-            msg = (
-                "It is strongly recommended to train T5Gemma models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
-            if is_torchdynamo_compiling():
-                raise ValueError(msg)
-            else:
-                logger.warning_once(msg)
 
         if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
             # get decoder inputs from shifting lm labels to the right

src/transformers/models/t5gemma/modular_t5gemma.py

@@ -37,7 +37,6 @@
     TransformersKwargs,
     auto_docstring,
     can_return_tuple,
-    is_torchdynamo_compiling,
     logging,
 )
 from ...utils.deprecation import deprecate_kwarg
@@ -921,15 +920,6 @@ def forward(
             config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
             (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
         """
-        if self.training and self.config._attn_implementation != "eager":
-            msg = (
-                "It is strongly recommended to train T5Gemma models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
-            if is_torchdynamo_compiling():
-                raise ValueError(msg)
-            else:
-                logger.warning_once(msg)
 
         if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
             # get decoder inputs from shifting lm labels to the right