Remove more kwargs

huggingface · May 16, 2024 · 76cfebe · 76cfebe
1 parent d12bcd0
commit 76cfebe
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Showing 7 changed files with 4 additions and 82 deletions.
diff --git a/src/transformers/models/cohere/modeling_cohere.py b/src/transformers/models/cohere/modeling_cohere.py
@@ -634,7 +634,6 @@ def forward(
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         """
         Args:
@@ -663,7 +662,6 @@ def forward(
             output_attentions=output_attentions,
             use_cache=use_cache,
             cache_position=cache_position,
-            **kwargs,
         )
 
         # Fully Connected

diff --git a/src/transformers/models/conditional_detr/modeling_conditional_detr.py b/src/transformers/models/conditional_detr/modeling_conditional_detr.py
@@ -556,9 +556,7 @@ def __init__(
     def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
         return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
 
-    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
+    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
         return tensor if object_queries is None else tensor + object_queries
 
     def forward(
@@ -569,12 +567,8 @@ def forward(
         key_value_states: Optional[torch.Tensor] = None,
         spatial_position_embeddings: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         """Input shape: Batch x Time x Channel"""
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         # if key_value_states are provided this layer is used as a cross-attention layer
         # for the decoder
         is_cross_attention = key_value_states is not None
@@ -798,7 +792,6 @@ def forward(
         attention_mask: torch.Tensor,
         object_queries: torch.Tensor = None,
         output_attentions: bool = False,
-        **kwargs,
     ):
         """
         Args:
@@ -812,9 +805,6 @@ def forward(
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         residual = hidden_states
         hidden_states, attn_weights = self.self_attn(
             hidden_states=hidden_states,
@@ -903,7 +893,6 @@ def forward(
         encoder_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
         is_first: Optional[bool] = False,
-        **kwargs,
     ):
         """
         Args:
@@ -926,9 +915,6 @@ def forward(
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         residual = hidden_states
 
         # ========== Begin of Self-Attention =============
@@ -1170,7 +1156,6 @@ def forward(
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs,
     ):
         r"""
         Args:
@@ -1197,9 +1182,6 @@ def forward(
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         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
@@ -1298,7 +1280,6 @@ def forward(
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs,
     ):
         r"""
         Args:
@@ -1335,9 +1316,6 @@ def forward(
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         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

diff --git a/src/transformers/models/detr/modeling_detr.py b/src/transformers/models/detr/modeling_detr.py
@@ -524,9 +524,7 @@ def __init__(
     def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
         return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
 
-    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
+    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
         return tensor if object_queries is None else tensor + object_queries
 
     def forward(
@@ -537,12 +535,8 @@ def forward(
         key_value_states: Optional[torch.Tensor] = None,
         spatial_position_embeddings: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         """Input shape: Batch x Time x Channel"""
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         # if key_value_states are provided this layer is used as a cross-attention layer
         # for the decoder
         is_cross_attention = key_value_states is not None
@@ -648,7 +642,6 @@ def forward(
         attention_mask: torch.Tensor,
         object_queries: torch.Tensor = None,
         output_attentions: bool = False,
-        **kwargs,
     ):
         """
         Args:
@@ -662,9 +655,6 @@ def forward(
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         residual = hidden_states
         hidden_states, attn_weights = self.self_attn(
             hidden_states=hidden_states,
@@ -734,7 +724,6 @@ def forward(
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-        **kwargs,
     ):
         """
         Args:
@@ -757,9 +746,6 @@ def forward(
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         residual = hidden_states
 
         # Self Attention
@@ -929,7 +915,6 @@ def forward(
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs,
     ):
         r"""
         Args:
@@ -956,9 +941,6 @@ def forward(
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         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
@@ -1050,7 +1032,6 @@ def forward(
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs,
     ):
         r"""
         Args:
@@ -1088,9 +1069,6 @@ def forward(
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         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

diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
@@ -103,9 +103,6 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, s
         self.register_buffer("inv_freq", inv_freq, persistent=False)
         # For BC we register cos and sin cached
         self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-        freqs = torch.outer(t, self.inv_freq)
 
     @torch.no_grad()
     def forward(self, x, position_ids):

diff --git a/src/transformers/models/maskformer/modeling_maskformer.py b/src/transformers/models/maskformer/modeling_maskformer.py
@@ -440,10 +440,7 @@ def __init__(
     def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
         return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
 
-    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
+    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
         return tensor if object_queries is None else tensor + object_queries
 
     def forward(
@@ -454,12 +451,8 @@ def forward(
         key_value_states: Optional[torch.Tensor] = None,
         spatial_position_embeddings: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         """Input shape: Batch x Time x Channel"""
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         # if key_value_states are provided this layer is used as a cross-attention layer
         # for the decoder
         is_cross_attention = key_value_states is not None
@@ -577,7 +570,6 @@ def forward(
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-        **kwargs,
     ):
         """
         Args:
@@ -600,9 +592,6 @@ def forward(
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         residual = hidden_states
 
         # Self Attention
@@ -690,7 +679,6 @@ def forward(
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs,
     ):
         r"""
         Args:
@@ -727,9 +715,6 @@ def forward(
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         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

diff --git a/src/transformers/models/olmo/modeling_olmo.py b/src/transformers/models/olmo/modeling_olmo.py
@@ -100,9 +100,6 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, s
         self.register_buffer("inv_freq", inv_freq, persistent=False)
         # For BC we register cos and sin cached
         self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-        freqs = torch.outer(t, self.inv_freq)
 
     @torch.no_grad()
     def forward(self, x, position_ids):

diff --git a/src/transformers/models/table_transformer/modeling_table_transformer.py b/src/transformers/models/table_transformer/modeling_table_transformer.py
@@ -461,10 +461,7 @@ def __init__(
     def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
         return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
 
-    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
+    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
         return tensor if object_queries is None else tensor + object_queries
 
     def forward(
@@ -475,12 +472,8 @@ def forward(
         key_value_states: Optional[torch.Tensor] = None,
         spatial_position_embeddings: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         """Input shape: Batch x Time x Channel"""
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         # if key_value_states are provided this layer is used as a cross-attention layer
         # for the decoder
         is_cross_attention = key_value_states is not None
@@ -981,7 +974,6 @@ def forward(
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs,
     ):
         r"""
         Args:
@@ -1019,9 +1011,6 @@ def forward(
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
         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