[Gradient Ckpting] Remove unnecessary attribute definitions

src/transformers/models/afmoe/modeling_afmoe.py

@@ -551,7 +551,6 @@ def __init__(self, config: AfmoeConfig):
         )
         self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = AfmoeRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         self.post_init()
 

src/transformers/models/afmoe/modular_afmoe.py

@@ -373,7 +373,6 @@ def __init__(self, config: AfmoeConfig):
         )
         self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = AfmoeRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         self.post_init()
 

src/transformers/models/aimv2/modeling_aimv2.py

@@ -330,7 +330,6 @@ def __init__(self, config: Aimv2Config):
         super().__init__()
         self.config = config
         self.layers = nn.ModuleList([Aimv2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     # Ignore copy
     @auto_docstring

src/transformers/models/align/modeling_align.py

@@ -760,7 +760,6 @@ def __init__(self, config):
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([AlignTextLayer(config) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     @can_return_tuple
     def forward(

src/transformers/models/altclip/modeling_altclip.py

@@ -373,7 +373,6 @@ def __init__(self, config):
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([AltRobertaLayer(config) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     @can_return_tuple
     def forward(
@@ -596,7 +595,6 @@ def __init__(self, config: AltCLIPConfig):
         super().__init__()
         self.config = config
         self.layers = nn.ModuleList([AltCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     @can_return_tuple
     def forward(

src/transformers/models/apertus/modeling_apertus.py

@@ -358,7 +358,6 @@ def __init__(self, config: ApertusConfig):
         )
         self.norm = ApertusRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = ApertusRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
         self.post_init()

src/transformers/models/arcee/modeling_arcee.py

@@ -360,7 +360,6 @@ def __init__(self, config: ArceeConfig):
         )
         self.norm = ArceeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = ArceeRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
         self.post_init()

src/transformers/models/aria/modeling_aria.py

@@ -694,7 +694,6 @@ def __init__(self, config: AriaTextConfig):
         )
         self.norm = AriaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = AriaTextRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
         self.post_init()

src/transformers/models/aria/modular_aria.py

@@ -1128,7 +1128,7 @@ def __init__(self, config: AriaTextConfig):
         self.layers = nn.ModuleList(
             [AriaTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self.gradient_checkpointing = False
+
         self.post_init()
 
 

src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py

@@ -274,7 +274,6 @@ def __init__(self, config: ASTConfig):
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([ASTLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     def forward(self, hidden_states: torch.Tensor) -> BaseModelOutput:
         for i, layer_module in enumerate(self.layer):

src/transformers/models/audioflamingo3/modeling_audioflamingo3.py

@@ -307,7 +307,6 @@ def __init__(self, config: AudioFlamingo3EncoderConfig):
         # Ignore copy
         self.avg_pooler = nn.AvgPool1d(2, stride=2)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/autoformer/modeling_autoformer.py

@@ -860,7 +860,6 @@ def __init__(self, config: AutoformerConfig):
         self.layers = nn.ModuleList([AutoformerEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -977,7 +976,6 @@ def __init__(self, config: AutoformerConfig):
         # https://github.com/thuml/Autoformer/blob/e6371e24f2ae2dd53e472edefdd5814c5176f864/models/Autoformer.py#L74
         self.seasonality_projection = nn.Linear(config.d_model, config.feature_size)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/bamba/modeling_bamba.py

@@ -1159,7 +1159,6 @@ def __init__(self, config: BambaConfig):
         self.final_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = BambaRotaryEmbedding(config=config)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/bamba/modular_bamba.py

@@ -833,7 +833,6 @@ def __init__(self, config: BambaConfig):
         self.final_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = BambaRotaryEmbedding(config=config)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/bark/modeling_bark.py

@@ -377,7 +377,6 @@ def __init__(self, config):
         self.layernorm_final = nn.LayerNorm(config.hidden_size, bias=config.bias)
 
         self.lm_head = nn.Linear(config.hidden_size, config.output_vocab_size, bias=False)
-        self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -900,7 +899,7 @@ def __init__(self, config):
                 for _ in range(config.n_codes_given, config.n_codes_total)
             ]
         )
-        self.gradient_checkpointing = False
+
         self.n_codes_total = config.n_codes_total
 
         # Initialize weights and apply final processing

src/transformers/models/bart/modeling_bart.py

@@ -531,7 +531,6 @@ def __init__(self, config: BartConfig):
         self.layers = nn.ModuleList([BartEncoderLayer(config, layer_idx=i) for i in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -674,7 +673,6 @@ def __init__(self, config: BartConfig):
 
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/beit/modeling_beit.py

@@ -610,7 +610,6 @@ def __init__(self, config: BeitConfig, window_size: tuple | None = None) -> None
                 for i in range(config.num_hidden_layers)
             ]
         )
-        self.gradient_checkpointing = False
 
     def forward(
         self,

src/transformers/models/bert/modeling_bert.py

@@ -618,7 +618,6 @@ def __init__(self, config, add_pooling_layer=True):
         """
         super().__init__(config)
         self.config = config
-        self.gradient_checkpointing = False
 
         self.embeddings = BertEmbeddings(config)
         self.encoder = BertEncoder(config)

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -490,7 +490,6 @@ class BertGenerationEncoder(BertGenerationPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.config = config
-        self.gradient_checkpointing = False
 
         self.embeddings = BertGenerationEmbeddings(config)
         self.encoder = BertEncoder(config)

src/transformers/models/big_bird/modeling_big_bird.py

@@ -1344,7 +1344,6 @@ def __init__(self, config):
         self.layer = nn.ModuleList(
             [BigBirdLayer(config, seed=layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self.gradient_checkpointing = False
 
     def set_attention_type(self, value: str):
         if value not in ["original_full", "block_sparse"]:

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -1582,7 +1582,6 @@ def __init__(self, config: BigBirdPegasusConfig):
         self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) for i in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -1855,7 +1854,6 @@ def __init__(self, config: BigBirdPegasusConfig):
         )
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/biogpt/modeling_biogpt.py

@@ -357,7 +357,6 @@ def __init__(self, config: BioGptConfig):
         self.layers = nn.ModuleList([BioGptDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
         self.layer_norm = nn.LayerNorm(self.embed_dim)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/biogpt/modular_biogpt.py

@@ -180,7 +180,6 @@ def __init__(self, config: BioGptConfig):
         self.layers = nn.ModuleList([BioGptDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
         self.layer_norm = nn.LayerNorm(self.embed_dim)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/bitnet/modeling_bitnet.py

@@ -358,7 +358,6 @@ def __init__(self, config: BitNetConfig):
         )
         self.norm = BitNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = BitNetRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
         self.post_init()

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -481,7 +481,6 @@ def __init__(self, config: BlenderbotConfig):
         self.layers = nn.ModuleList([BlenderbotEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layer_norm = nn.LayerNorm(config.d_model)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -627,7 +626,6 @@ def __init__(self, config: BlenderbotConfig):
         )
         self.layer_norm = nn.LayerNorm(config.d_model)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -473,7 +473,6 @@ def __init__(self, config: BlenderbotSmallConfig):
         self.layers = nn.ModuleList([BlenderbotSmallEncoderLayer(config) for _ in range(config.encoder_layers)])
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -615,7 +614,6 @@ def __init__(self, config: BlenderbotSmallConfig):
         )
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
 
-        self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/blip/modeling_blip.py

@@ -442,7 +442,6 @@ def __init__(self, config: BlipConfig):
         super().__init__()
         self.config = config
         self.layers = nn.ModuleList([BlipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     @auto_docstring
     def forward(

src/transformers/models/blip/modeling_blip_text.py

@@ -349,7 +349,6 @@ def __init__(self, config):
         super().__init__()
         self.config = config
         self.layer = nn.ModuleList([BlipTextLayer(config, i) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     def forward(
         self,

src/transformers/models/blip_2/modeling_blip_2.py

@@ -461,7 +461,6 @@ def __init__(self, config: Blip2Config):
         super().__init__()
         self.config = config
         self.layers = nn.ModuleList([Blip2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
 
     @auto_docstring
     def forward(
@@ -797,7 +796,6 @@ def __init__(self, config):
         self.layer = nn.ModuleList(
             [Blip2QFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self.gradient_checkpointing = False
 
     @can_return_tuple
     def forward(

src/transformers/models/bloom/modeling_bloom.py

@@ -429,8 +429,6 @@ def __init__(self, config: BloomConfig):
         # Final Layer Norm
         self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
 
-        self.gradient_checkpointing = False
-
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/blt/modeling_blt.py

@@ -608,7 +608,7 @@ class BltLocalEncoder(BltPreTrainedModel):
 
     def __init__(self, config: BltLocalEncoderConfig):
         super().__init__(config)
-        self.gradient_checkpointing = False
+
         self.config = config
         self.layers = nn.ModuleList(
             [BltTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
@@ -720,7 +720,7 @@ class BltLocalDecoder(BltPreTrainedModel):
 
     def __init__(self, config: BltLocalDecoderConfig):
         super().__init__(config)
-        self.gradient_checkpointing = False
+
         self.config = config
         self.cross_attn_decoder = True
         self.layers = nn.ModuleList(
@@ -1196,7 +1196,6 @@ def _prepare_patch_cross_attention_mask(
 class BltModel(BltPreTrainedModel):
     def __init__(self, config: BltConfig):
         super().__init__(config)
-        self.gradient_checkpointing = False
 
         self.config = config
         self.local_encoder = BltLocalEncoder(config.encoder_config)

src/transformers/models/blt/modular_blt.py

@@ -535,7 +535,7 @@ class BltLocalEncoder(BltPreTrainedModel):
 
     def __init__(self, config: BltLocalEncoderConfig):
         super().__init__(config)
-        self.gradient_checkpointing = False
+
         self.config = config
         self.layers = nn.ModuleList(
             [BltTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
@@ -647,7 +647,7 @@ class BltLocalDecoder(BltPreTrainedModel):
 
     def __init__(self, config: BltLocalDecoderConfig):
         super().__init__(config)
-        self.gradient_checkpointing = False
+
         self.config = config
         self.cross_attn_decoder = True
         self.layers = nn.ModuleList(
@@ -916,7 +916,6 @@ def patch_lengths_from_entropies(
 class BltModel(BltPreTrainedModel):
     def __init__(self, config: BltConfig):
         super().__init__(config)
-        self.gradient_checkpointing = False
 
         self.config = config
         self.local_encoder = BltLocalEncoder(config.encoder_config)

src/transformers/models/bridgetower/modeling_bridgetower.py

@@ -992,7 +992,6 @@ def __init__(self, config, add_pooling_layer=True):
         """
         super().__init__(config)
         self.config = config
-        self.gradient_checkpointing = False
 
         self.embeddings = BridgeTowerTextEmbeddings(config)
         self.encoder = BridgeTowerTextEncoder(config)

src/transformers/models/camembert/modeling_camembert.py

@@ -597,7 +597,6 @@ def __init__(self, config, add_pooling_layer=True):
         """
         super().__init__(config)
         self.config = config
-        self.gradient_checkpointing = False
 
         self.embeddings = CamembertEmbeddings(config)
         self.encoder = CamembertEncoder(config)

src/transformers/models/canine/modeling_canine.py

@@ -613,7 +613,6 @@ def __init__(
                 for _ in range(config.num_hidden_layers)
             ]
         )
-        self.gradient_checkpointing = False
 
     def forward(
         self,

src/transformers/models/chameleon/modeling_chameleon.py

@@ -867,7 +867,6 @@ def __init__(self, config: ChameleonConfig):
         self.norm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.vqmodel = ChameleonVQVAE._from_config(config.vq_config)
         self.rotary_emb = ChameleonRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
         self.post_init()