update models based on qwen2

src/transformers/models/dots1/modeling_dots1.py

@@ -35,13 +35,11 @@ from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 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, logging
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import check_model_inputs
 from .configuration_dots1 import Dots1Config
 
 
-logger = logging.get_logger(__name__)
-
-
 @use_kernel_forward_from_hub("RMSNorm")
 class Dots1RMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
@@ -469,7 +467,7 @@ class Dots1Model(Dots1PreTrainedModel):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    @can_return_tuple
+    @check_model_inputs
     @auto_docstring
     def forward(
         self,
@@ -479,30 +477,12 @@ class Dots1Model(Dots1PreTrainedModel):
         past_key_values: Optional[Cache] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
     ) -> BaseModelOutputWithPast:
-        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
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
-        if not isinstance(past_key_values, (type(None), Cache)):
-            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
-
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
@@ -541,42 +521,22 @@ class Dots1Model(Dots1PreTrainedModel):
         # create position embeddings to be shared across the decoder layers
         position_embeddings = self.rotary_emb(hidden_states, position_ids)
 
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
         for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
+            hidden_states = decoder_layer(
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
                 hidden_states,
                 attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                 position_ids=position_ids,
                 past_key_value=past_key_values,
-                output_attentions=output_attentions,
                 use_cache=use_cache,
                 cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **flash_attn_kwargs,
             )
 
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
         hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
         )
 
 

src/transformers/models/qwen2/modeling_qwen2.py

@@ -26,7 +26,7 @@ from ...modeling_outputs import (
 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, logging
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, check_model_inputs, logging
 from .configuration_qwen2 import Qwen2Config
 
 
@@ -343,7 +343,7 @@ class Qwen2Model(Qwen2PreTrainedModel):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    @can_return_tuple
+    @check_model_inputs
     @auto_docstring
     def forward(
         self,
@@ -353,30 +353,12 @@ class Qwen2Model(Qwen2PreTrainedModel):
         past_key_values: Optional[Cache] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
     ) -> BaseModelOutputWithPast:
-        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
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
-        if not isinstance(past_key_values, (type(None), Cache)):
-            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
-
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
@@ -415,42 +397,22 @@ class Qwen2Model(Qwen2PreTrainedModel):
         # create position embeddings to be shared across the decoder layers
         position_embeddings = self.rotary_emb(hidden_states, position_ids)
 
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
         for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
+            hidden_states = decoder_layer(
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
                 hidden_states,
                 attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                 position_ids=position_ids,
                 past_key_value=past_key_values,
-                output_attentions=output_attentions,
                 use_cache=use_cache,
                 cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **flash_attn_kwargs,
             )
 
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
         hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
         )
 
 

src/transformers/models/qwen3/modeling_qwen3.py

@@ -42,6 +42,7 @@ 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, logging
+from ...utils.generic import check_model_inputs
 from .configuration_qwen3 import Qwen3Config
 
 
@@ -369,7 +370,7 @@ class Qwen3Model(Qwen3PreTrainedModel):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    @can_return_tuple
+    @check_model_inputs
     @auto_docstring
     def forward(
         self,
@@ -379,30 +380,12 @@ class Qwen3Model(Qwen3PreTrainedModel):
         past_key_values: Optional[Cache] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
     ) -> BaseModelOutputWithPast:
-        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
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
-        if not isinstance(past_key_values, (type(None), Cache)):
-            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
-
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
@@ -441,42 +424,22 @@ class Qwen3Model(Qwen3PreTrainedModel):
         # create position embeddings to be shared across the decoder layers
         position_embeddings = self.rotary_emb(hidden_states, position_ids)
 
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
         for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
+            hidden_states = decoder_layer(
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
                 hidden_states,
                 attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                 position_ids=position_ids,
                 past_key_value=past_key_values,
-                output_attentions=output_attentions,
                 use_cache=use_cache,
                 cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **flash_attn_kwargs,
             )
 
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
         hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
         )
 
 

src/transformers/models/smollm3/modeling_smollm3.py

@@ -42,6 +42,7 @@ 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, logging
+from ...utils.generic import check_model_inputs
 from .configuration_smollm3 import SmolLM3Config
 
 
@@ -372,7 +373,7 @@ class SmolLM3Model(SmolLM3PreTrainedModel):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    @can_return_tuple
+    @check_model_inputs
     @auto_docstring
     def forward(
         self,
@@ -382,30 +383,12 @@ class SmolLM3Model(SmolLM3PreTrainedModel):
         past_key_values: Optional[Cache] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
     ) -> BaseModelOutputWithPast:
-        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
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
-        if not isinstance(past_key_values, (type(None), Cache)):
-            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
-
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
@@ -444,42 +427,22 @@ class SmolLM3Model(SmolLM3PreTrainedModel):
         # create position embeddings to be shared across the decoder layers
         position_embeddings = self.rotary_emb(hidden_states, position_ids)
 
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
         for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
+            hidden_states = decoder_layer(
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
                 hidden_states,
                 attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                 position_ids=position_ids,
                 past_key_value=past_key_values,
-                output_attentions=output_attentions,
                 use_cache=use_cache,
                 cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **flash_attn_kwargs,
             )
 
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
         hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
         )