Clean up unused padding_idx variables across many model definitions (#13240)

tlrmchlsmth · web-flow · commit 4f5b059f146a · 2025-03-04T21:27:00.000Z
Signed-off-by: Tyler Michael Smith &lt;tyler@neuralmagic.com&gt;
diff --git a/vllm/model_executor/models/arctic.py b/vllm/model_executor/models/arctic.py
@@ -370,7 +370,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.embed_tokens = VocabParallelEmbedding(
             self.vocab_size,
diff --git a/vllm/model_executor/models/baichuan.py b/vllm/model_executor/models/baichuan.py
@@ -267,7 +267,6 @@ def __init__(
         quant_config = vllm_config.quant_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(
diff --git a/vllm/model_executor/models/bart.py b/vllm/model_executor/models/bart.py
@@ -725,7 +725,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.config = config
 
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/chameleon.py b/vllm/model_executor/models/chameleon.py
@@ -851,7 +851,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.embed_tokens = VocabParallelEmbedding(
             self.vocab_size,
diff --git a/vllm/model_executor/models/deepseek.py b/vllm/model_executor/models/deepseek.py
@@ -339,7 +339,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(
diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
@@ -570,7 +570,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         if get_pp_group().is_first_rank:
diff --git a/vllm/model_executor/models/exaone.py b/vllm/model_executor/models/exaone.py
@@ -313,7 +313,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         lora_config = vllm_config.lora_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         lora_vocab = ((lora_config.lora_extra_vocab_size *
                        (lora_config.max_loras or 1)) if lora_config else 0)
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/florence2.py b/vllm/model_executor/models/florence2.py
@@ -592,7 +592,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.config = config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model)
diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py
@@ -255,7 +255,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.config = config
         self.multimodal_config = multimodal_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.text_config.vocab_size
         self.image_token_id = _IMAGE_TOKEN_ID
         self.image_feature_size = config.patch_size**2 * config.num_channels
diff --git a/vllm/model_executor/models/granite.py b/vllm/model_executor/models/granite.py
@@ -260,7 +260,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         lora_config = vllm_config.lora_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py
@@ -252,7 +252,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
 
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/idefics3.py b/vllm/model_executor/models/idefics3.py
@@ -404,7 +404,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
 
         self.config = config
-        self.padding_idx = self.config.text_config.pad_token_id
         self.vocab_size = self.config.text_config.vocab_size
         self.vision_model = Idefics3VisionTransformer(
             config.vision_config,
diff --git a/vllm/model_executor/models/internlm2.py b/vllm/model_executor/models/internlm2.py
@@ -261,7 +261,6 @@ def __init__(
         quant_config = vllm_config.quant_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.tok_embeddings = VocabParallelEmbedding(
             config.vocab_size,
diff --git a/vllm/model_executor/models/jamba.py b/vllm/model_executor/models/jamba.py
@@ -271,7 +271,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         lora_config = vllm_config.lora_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         lora_vocab = ((lora_config.lora_extra_vocab_size *
                        (lora_config.max_loras or 1)) if lora_config else 0)
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py
@@ -302,7 +302,6 @@ def __init__(self,
 
         self.config = config
         self.quant_config = quant_config
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/mamba.py b/vllm/model_executor/models/mamba.py
@@ -90,7 +90,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         is_lora_enabled = bool(lora_config)
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         lora_vocab = ((lora_config.lora_extra_vocab_size *
                        (lora_config.max_loras or 1)) if lora_config else 0)
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/minicpm.py b/vllm/model_executor/models/minicpm.py
@@ -365,7 +365,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.config = config
         self.cache_config = cache_config
         self.quant_config = quant_config
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/mixtral.py b/vllm/model_executor/models/mixtral.py
@@ -254,7 +254,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
 
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/mixtral_quant.py b/vllm/model_executor/models/mixtral_quant.py
@@ -302,7 +302,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(
diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py
@@ -1031,7 +1031,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size + 8,
                                                    config.hidden_size)
diff --git a/vllm/model_executor/models/nemotron.py b/vllm/model_executor/models/nemotron.py
@@ -300,7 +300,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         lora_config = vllm_config.lora_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/olmoe.py b/vllm/model_executor/models/olmoe.py
@@ -252,7 +252,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(
diff --git a/vllm/model_executor/models/opt.py b/vllm/model_executor/models/opt.py
@@ -200,7 +200,6 @@ def __init__(
     ):
         super().__init__()
         self.config = config
-        self.padding_idx = config.pad_token_id
         self.max_target_positions = config.max_position_embeddings
         self.vocab_size = config.vocab_size
 
diff --git a/vllm/model_executor/models/orion.py b/vllm/model_executor/models/orion.py
@@ -217,7 +217,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
diff --git a/vllm/model_executor/models/phimoe.py b/vllm/model_executor/models/phimoe.py
@@ -441,7 +441,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
 
-        self.padding_idx = config.pad_token_id
         lora_vocab = ((lora_config.lora_extra_vocab_size *
                        (lora_config.max_loras or 1)) if lora_config else 0)
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/qwen2.py b/vllm/model_executor/models/qwen2.py
@@ -284,7 +284,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.config = config
         self.quant_config = quant_config
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         if get_pp_group().is_first_rank or (config.tie_word_embeddings
diff --git a/vllm/model_executor/models/qwen2_moe.py b/vllm/model_executor/models/qwen2_moe.py
@@ -325,7 +325,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(
diff --git a/vllm/model_executor/models/solar.py b/vllm/model_executor/models/solar.py
@@ -269,7 +269,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         lora_config = vllm_config.lora_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         lora_vocab = ((lora_config.lora_extra_vocab_size *
                        (lora_config.max_loras or 1)) if lora_config else 0)
         self.vocab_size = config.vocab_size + lora_vocab
diff --git a/vllm/model_executor/models/starcoder2.py b/vllm/model_executor/models/starcoder2.py
@@ -212,10 +212,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         quant_config = vllm_config.quant_config
 
         self.config = config
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
-        # TODO: consider padding_idx (currently removed)
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py
@@ -49,10 +49,7 @@ class WhisperAudioInputs(TypedDict):
 
 class WhisperPositionalEmbedding(nn.Embedding):
 
-    def __init__(self,
-                 num_positions: int,
-                 embedding_dim: int,
-                 padding_idx: Optional[int] = None):
+    def __init__(self, num_positions: int, embedding_dim: int):
         super().__init__(num_positions, embedding_dim)
 
     def forward(self, position_ids):
@@ -359,7 +356,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         embed_dim = config.d_model
         self.num_mel_bins = config.num_mel_bins
-        self.padding_idx = config.pad_token_id
         self.max_source_positions = config.max_source_positions
         self.embed_scale = (math.sqrt(embed_dim)
                             if config.scale_embedding else 1.0)
