diff --git a/src/transformers/models/deprecated/open_llama/modeling_open_llama.py b/src/transformers/models/deprecated/open_llama/modeling_open_llama.py
index 6853f5333f137c..3ace323e822414 100644
--- a/src/transformers/models/deprecated/open_llama/modeling_open_llama.py
+++ b/src/transformers/models/deprecated/open_llama/modeling_open_llama.py
@@ -560,7 +560,7 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
     def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
         # create causal mask
         # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
diff --git a/src/transformers/models/falcon/modeling_falcon.py b/src/transformers/models/falcon/modeling_falcon.py
index e9dca6df989472..6eaeed4199771c 100644
--- a/src/transformers/models/falcon/modeling_falcon.py
+++ b/src/transformers/models/falcon/modeling_falcon.py
@@ -15,6 +15,7 @@
 """PyTorch Falcon model."""
 
 import math
+import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -76,9 +77,9 @@ def rotate_half(x):
 
 
 # Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(padding_mask):
-    seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten()
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
     return (
@@ -88,6 +89,143 @@ def _get_unpad_data(padding_mask):
     )
 
 
+# Copied from transformers.models.llama.modeling_llama.AttnMaskConverter
+class AttnMaskConverter:
+    """
+    A utility attention mask class that allows:
+        - Create a causal 4d mask
+        - Create a causal 4d mask with slided window
+        - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
+          key_value_length) that can be multiplied with attention scores
+
+    Parameters:
+        is_causal (`bool`):
+            Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
+
+        sliding_window (`int`, *optional*):
+            Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
+    """
+
+    def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
+        self.is_causal = is_causal
+        self.sliding_window = sliding_window
+
+    def to_causal_4d(
+        self,
+        batch_size: int,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype = torch.float32,
+        device: Union[torch.device, "str"] = "cpu",
+    ) -> torch.Tensor:
+        """
+        Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
+        bias to upper right hand triangular matrix (causal mask).
+        """
+        if not self.is_causal:
+            raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
+
+        # If shape is not cached, create a new causal mask and cache it
+        input_shape = (batch_size, query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if input_shape[-1] > 1 or self.sliding_window is not None:
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+
+        return causal_4d_mask
+
+    def to_4d(
+        self,
+        attention_mask_2d: torch.Tensor,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype = torch.float32,
+    ) -> torch.Tensor:
+        """
+        Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
+        key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
+        causal, a causal mask will be added.
+        """
+        input_shape = (attention_mask_2d.shape[0], query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=attention_mask_2d.device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+        elif self.sliding_window is not None:
+            raise NotImplementedError("Sliding window is currently only implemented for causal masking")
+
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
+            attention_mask_2d.device
+        )
+        expanded_4d_mask = expanded_attn_mask if causal_4d_mask is None else expanded_attn_mask + causal_4d_mask
+
+        return expanded_4d_mask
+
+    def _make_causal_mask(
+        self,
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+        sliding_window: Optional[int] = None,
+    ):
+        """
+        Make causal mask used for bi-directional self-attention.
+        """
+        bsz, tgt_len = input_ids_shape
+        mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+        mask_cond = torch.arange(mask.size(-1), device=device)
+        mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+
+        mask = mask.to(dtype)
+
+        if past_key_values_length > 0:
+            mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+
+        # add lower triangular sliding window mask if necessary
+        if sliding_window is not None:
+            diagonal = past_key_values_length - sliding_window + 1
+
+            context_mask = 1 - torch.triu(torch.ones_like(mask, dtype=torch.int), diagonal=diagonal)
+            mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min)
+
+        return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+    def _expand_mask(self, mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+        """
+        Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+        """
+        bsz, src_len = mask.size()
+        tgt_len = tgt_len if tgt_len is not None else src_len
+
+        expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+        inverted_mask = 1.0 - expanded_mask
+
+        return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
 # TODO (joao): Is this the same implementation as in Llama? If so, let's make them the same and add the copy facilities
 class FalconRotaryEmbedding(nn.Module):
     """Implementation of RotaryEmbedding from GPT-NeoX.
@@ -311,6 +449,7 @@ def __init__(self, config: FalconConfig):
         self.head_dim = self.hidden_size // self.num_heads
         self.split_size = self.hidden_size
         self.hidden_dropout = config.hidden_dropout
+        self.is_causal = True
 
         if self.head_dim * self.num_heads != self.hidden_size:
             raise ValueError(
@@ -431,8 +570,13 @@ def forward(
         head_mask: Optional[torch.Tensor] = None,
         use_cache: bool = False,
         output_attentions: bool = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
         fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
         num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
         # 3 x [batch_size, seq_length, num_heads, head_dim]
@@ -465,9 +609,6 @@ def forward(
         else:
             present = None
 
-        float_min = torch.finfo(query_layer.dtype).min
-        attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float_min).to(query_layer.dtype)
-
         query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
         key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
         value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
@@ -482,16 +623,14 @@ def forward(
                 )
 
                 attn_output = F.scaled_dot_product_attention(
-                    query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False
+                    query_layer_, key_layer_, value_layer_, attention_mask, 0.0, is_causal=False
                 )
                 attention_scores = None
             else:
                 attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
                 attention_scores /= math.sqrt(self.head_dim)
 
-                attention_scores = F.softmax(
-                    attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
-                )
+                attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
                 attn_output = attention_scores @ value_layer_
 
             attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
@@ -517,12 +656,12 @@ def forward(
             if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
                 attention_scores = attention_scores.to(torch.float32)
             # Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
-            # adding (alibi * self.inv_norm_factor) to attention_mask_float. I think this would be mathematically
+            # adding (alibi * self.inv_norm_factor) to attention_mask. I think this would be mathematically
             # equivalent and more performant, but there might be a numerical difference. If you're reading this
             # and you'd like to experiment and maybe file a PR, feel free!
             attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
             attention_logits *= self.inv_norm_factor
-            attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1, dtype=hidden_states.dtype)
+            attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
             # [batch_size, num_heads, q_length, kv_length]
             attention_probs = self.attention_dropout(attention_probs)
 
@@ -563,8 +702,16 @@ def forward(
         head_mask: Optional[torch.Tensor] = None,
         use_cache: bool = False,
         output_attentions: bool = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
         fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
         num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
         # 3 x [batch_size, seq_length, num_heads, head_dim]
@@ -630,7 +777,7 @@ def forward(
             value_layer = value_layer.to(target_dtype)
 
         attn_output = self._flash_attention_forward(
-            query_layer, key_layer, value_layer, padding_mask, query_length, dropout=attn_dropout
+            query_layer, key_layer, value_layer, attention_mask, query_length, dropout=attn_dropout
         )
 
         attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
@@ -643,7 +790,7 @@ def forward(
 
     # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
     def _flash_attention_forward(
-        self, query_states, key_states, value_states, padding_mask, query_length, dropout=0.0, softmax_scale=None
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
     ):
         """
         Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
@@ -656,7 +803,7 @@ def _flash_attention_forward(
                 Input key states to be passed to Flash Attention API
             value_states (`torch.Tensor`):
                 Input value states to be passed to Flash Attention API
-            padding_mask (`torch.Tensor`):
+            attention_mask (`torch.Tensor`):
                 The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                 position of padding tokens and 1 for the position of non-padding tokens.
             dropout (`int`, *optional*):
@@ -665,10 +812,10 @@ def _flash_attention_forward(
                 The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
         """
         # Contains at least one padding token in the sequence
-        if padding_mask is not None:
+        if attention_mask is not None:
             batch_size = query_states.shape[0]
             query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, padding_mask, query_length
+                query_states, key_states, value_states, attention_mask, query_length
             )
 
             cu_seqlens_q, cu_seqlens_k = cu_seq_lens
@@ -684,7 +831,7 @@ def _flash_attention_forward(
                 max_seqlen_k=max_seqlen_in_batch_k,
                 dropout_p=dropout,
                 softmax_scale=softmax_scale,
-                causal=True,
+                causal=self.is_causal,
             )
 
             attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
@@ -696,8 +843,8 @@ def _flash_attention_forward(
         return attn_output
 
     # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
-    def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
         batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
 
         key_layer = index_first_axis(
@@ -722,8 +869,8 @@ def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_l
             query_layer = query_layer.squeeze(1)
         else:
             # The -q_len: slice assumes left padding.
-            padding_mask = padding_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask)
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
 
         return (
             query_layer,
@@ -752,7 +899,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class FalconDecoderLayer(nn.Module):
-    def __init__(self, config: FalconConfig):
+    def __init__(self, config: FalconConfig, attn_mask_converter=None):
         super().__init__()
         hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
@@ -786,8 +933,13 @@ def forward(
         head_mask: Optional[torch.Tensor] = None,
         use_cache: bool = False,
         output_attentions: bool = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
         residual = hidden_states
 
         if self.config.new_decoder_architecture:
@@ -806,7 +958,7 @@ def forward(
             head_mask=head_mask,
             use_cache=use_cache,
             output_attentions=output_attentions,
-            padding_mask=padding_mask,
+            **kwargs,
         )
 
         attention_output = attn_outputs[0]
@@ -1001,6 +1153,10 @@ def __init__(self, config: FalconConfig):
         # Embedding + LN Embedding
         self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
 
+        # create attention mask cache that trickles down to each attention layer
+        # so that the attention_mask cache can be shared among layers
+        self.attn_mask_converter = AttnMaskConverter(is_causal=True)
+
         # Transformer blocks
         self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)])
 
@@ -1015,37 +1171,6 @@ def __init__(self, config: FalconConfig):
     def get_input_embeddings(self):
         return self.word_embeddings
 
-    @staticmethod
-    def _prepare_attn_mask(
-        attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
-    ) -> torch.BoolTensor:
-        # Create a causal mask
-        # The attention mask we receive as input should cover the whole extended sequence, including any past
-        # cache, so its shape should be [batch_size, seq_length + past_key_values_length]
-        # The output shape will be [batch_size, 1, seq_length, seq_length + past_key_values_length]
-        if input_shape[1] + past_key_values_length != attention_mask.shape[1]:
-            raise ValueError(
-                "Attention mask shape should be (batch_size, seq_length + past_key_values_length)"
-                f" but is {attention_mask.shape} with input_ids shape {input_shape} and past length"
-                f" {past_key_values_length}."
-            )
-        combined_attention_mask = None
-        device = attention_mask.device
-        _, seq_length = input_shape
-
-        if seq_length > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape, device=device, past_key_values_length=past_key_values_length
-            )
-
-        # [batch_size, seq_length + past_key_values_length] -> [batch_size, 1, seq_length, seq_length + past_key_values_length]
-        expanded_attn_mask = _expand_mask(attention_mask, past_key_values_length=past_key_values_length)
-        combined_attention_mask = (
-            expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
-        )
-
-        return combined_attention_mask
-
     def set_input_embeddings(self, new_embeddings: torch.Tensor):
         self.word_embeddings = new_embeddings
 
@@ -1114,19 +1239,16 @@ def forward(
         past_key_values_length = 0
         if past_key_values[0] is not None:
             past_key_values_length = past_key_values[0][0].shape[1]  # 1 because RW-cache, not standard format
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=hidden_states.device)
-            padding_mask = None
-        else:
-            attention_mask = attention_mask.to(hidden_states.device)
-
-            if 0 in attention_mask:
-                padding_mask = attention_mask
-            else:
-                padding_mask = None
 
         if self.use_alibi:
-            alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
+            mask = (
+                torch.ones(
+                    (batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long
+                )
+                if attention_mask is None
+                else attention_mask
+            )
+            alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype)
         else:
             alibi = None
             if position_ids is None:
@@ -1136,11 +1258,20 @@ def forward(
                 )
                 position_ids = position_ids.unsqueeze(0)
 
-        causal_mask = self._prepare_attn_mask(
-            attention_mask,
-            input_shape=(batch_size, seq_length),
-            past_key_values_length=past_key_values_length,
-        )
+        if getattr(self.config, "_flash_attn_2_enabled", False):
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            key_value_length = seq_length + past_key_values_length
+            # 4d mask is passed through the layers
+            if attention_mask is not None:
+                attention_mask = self.attn_mask_converter.to_4d(
+                    attention_mask, seq_length, key_value_length, dtype=inputs_embeds.dtype
+                )
+            else:
+                attention_mask = self.attn_mask_converter.to_causal_4d(
+                    batch_size, seq_length, key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+                )
 
         for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
             if output_hidden_states:
@@ -1159,22 +1290,20 @@ def custom_forward(*inputs):
                     create_custom_forward(block),
                     hidden_states,
                     alibi,
-                    causal_mask,
+                    attention_mask,
                     position_ids,
                     head_mask[i],
-                    padding_mask,
                 )
             else:
                 outputs = block(
                     hidden_states,
                     layer_past=layer_past,
-                    attention_mask=causal_mask,
+                    attention_mask=attention_mask,
                     position_ids=position_ids,
                     head_mask=head_mask[i],
                     use_cache=use_cache,
                     output_attentions=output_attentions,
                     alibi=alibi,
-                    padding_mask=padding_mask,
                 )
 
             hidden_states = outputs[0]
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index b67719ac327162..541455d86afde8 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -19,6 +19,7 @@
 # limitations under the License.
 """ PyTorch LLaMA model."""
 import math
+import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -51,9 +52,9 @@
 _CONFIG_FOR_DOC = "LlamaConfig"
 
 
-def _get_unpad_data(padding_mask):
-    seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten()
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
     return (
@@ -63,37 +64,140 @@ def _get_unpad_data(padding_mask):
     )
 
 
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
+class AttnMaskConverter:
     """
-    Make causal mask used for bi-directional self-attention.
+    A utility attention mask class that allows:
+        - Create a causal 4d mask
+        - Create a causal 4d mask with slided window
+        - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
+          key_value_length) that can be multiplied with attention scores
+
+    Parameters:
+        is_causal (`bool`):
+            Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
+
+        sliding_window (`int`, *optional*):
+            Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
     """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
 
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+    def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
+        self.is_causal = is_causal
+        self.sliding_window = sliding_window
 
+    def to_causal_4d(
+        self,
+        batch_size: int,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype = torch.float32,
+        device: Union[torch.device, "str"] = "cpu",
+    ) -> torch.Tensor:
+        """
+        Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
+        bias to upper right hand triangular matrix (causal mask).
+        """
+        if not self.is_causal:
+            raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
 
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
+        # If shape is not cached, create a new causal mask and cache it
+        input_shape = (batch_size, query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if input_shape[-1] > 1 or self.sliding_window is not None:
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+
+        return causal_4d_mask
+
+    def to_4d(
+        self,
+        attention_mask_2d: torch.Tensor,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype = torch.float32,
+    ) -> torch.Tensor:
+        """
+        Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
+        key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
+        causal, a causal mask will be added.
+        """
+        input_shape = (attention_mask_2d.shape[0], query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=attention_mask_2d.device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+        elif self.sliding_window is not None:
+            raise NotImplementedError("Sliding window is currently only implemented for causal masking")
+
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
+            attention_mask_2d.device
+        )
+        expanded_4d_mask = expanded_attn_mask if causal_4d_mask is None else expanded_attn_mask + causal_4d_mask
 
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+        return expanded_4d_mask
 
-    inverted_mask = 1.0 - expanded_mask
+    def _make_causal_mask(
+        self,
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+        sliding_window: Optional[int] = None,
+    ):
+        """
+        Make causal mask used for bi-directional self-attention.
+        """
+        bsz, tgt_len = input_ids_shape
+        mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+        mask_cond = torch.arange(mask.size(-1), device=device)
+        mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
 
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+        mask = mask.to(dtype)
+
+        if past_key_values_length > 0:
+            mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+
+        # add lower triangular sliding window mask if necessary
+        if sliding_window is not None:
+            diagonal = past_key_values_length - sliding_window + 1
+
+            context_mask = 1 - torch.triu(torch.ones_like(mask, dtype=torch.int), diagonal=diagonal)
+            mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min)
+
+        return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+    def _expand_mask(self, mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+        """
+        Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+        """
+        bsz, src_len = mask.size()
+        tgt_len = tgt_len if tgt_len is not None else src_len
+
+        expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+        inverted_mask = 1.0 - expanded_mask
+
+        return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
 
 
 class LlamaRMSNorm(nn.Module):
@@ -272,6 +376,7 @@ def __init__(self, config: LlamaConfig):
         self.num_key_value_groups = self.num_heads // self.num_key_value_heads
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
+        self.is_causal = True
 
         if (self.head_dim * self.num_heads) != self.hidden_size:
             raise ValueError(
@@ -322,8 +427,13 @@ def forward(
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
         bsz, q_len, _ = hidden_states.size()
 
         if self.config.pretraining_tp > 1:
@@ -420,14 +530,22 @@ class LlamaFlashAttention2(LlamaAttention):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         # LlamaFlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
         output_attentions = False
 
         bsz, q_len, _ = hidden_states.size()
@@ -492,7 +610,7 @@ def forward(
             value_states = value_states.to(target_dtype)
 
         attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, padding_mask, q_len, dropout=dropout_rate
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
         )
 
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
@@ -504,7 +622,7 @@ def forward(
         return attn_output, attn_weights, past_key_value
 
     def _flash_attention_forward(
-        self, query_states, key_states, value_states, padding_mask, query_length, dropout=0.0, softmax_scale=None
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
     ):
         """
         Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
@@ -517,7 +635,7 @@ def _flash_attention_forward(
                 Input key states to be passed to Flash Attention API
             value_states (`torch.Tensor`):
                 Input value states to be passed to Flash Attention API
-            padding_mask (`torch.Tensor`):
+            attention_mask (`torch.Tensor`):
                 The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                 position of padding tokens and 1 for the position of non-padding tokens.
             dropout (`int`, *optional*):
@@ -526,10 +644,10 @@ def _flash_attention_forward(
                 The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
         """
         # Contains at least one padding token in the sequence
-        if padding_mask is not None:
+        if attention_mask is not None:
             batch_size = query_states.shape[0]
             query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, padding_mask, query_length
+                query_states, key_states, value_states, attention_mask, query_length
             )
 
             cu_seqlens_q, cu_seqlens_k = cu_seq_lens
@@ -545,7 +663,7 @@ def _flash_attention_forward(
                 max_seqlen_k=max_seqlen_in_batch_k,
                 dropout_p=dropout,
                 softmax_scale=softmax_scale,
-                causal=True,
+                causal=self.is_causal,
             )
 
             attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
@@ -556,8 +674,8 @@ def _flash_attention_forward(
 
         return attn_output
 
-    def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
         batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
 
         key_layer = index_first_axis(
@@ -582,8 +700,8 @@ def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_l
             query_layer = query_layer.squeeze(1)
         else:
             # The -q_len: slice assumes left padding.
-            padding_mask = padding_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask)
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
 
         return (
             query_layer,
@@ -616,13 +734,13 @@ def forward(
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+                `(batch, sequence_length)` where padding elements are indicated by 0.
             output_attentions (`bool`, *optional*):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
@@ -631,6 +749,10 @@ def forward(
                 (see `past_key_values`).
             past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
         """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
 
         residual = hidden_states
 
@@ -644,7 +766,7 @@ def forward(
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
-            padding_mask=padding_mask,
+            **kwargs,
         )
         hidden_states = residual + hidden_states
 
@@ -791,6 +913,10 @@ def __init__(self, config: LlamaConfig):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
+        # create attention mask cache that trickles down to each attention layer
+        # so that the attention_mask cache can be shared among layers
+        self.attn_mask_converter = AttnMaskConverter(is_causal=True)
+
         self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
         self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -805,30 +931,6 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
     @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
     def forward(
         self,
@@ -854,18 +956,15 @@ def forward(
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
+            batch_size, seq_length = input_ids.shape[:2]
         elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
+            batch_size, seq_length = inputs_embeds.shape[:2]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        seq_length_with_past = seq_length
         past_key_values_length = 0
-
         if past_key_values is not None:
             past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
 
         if position_ids is None:
             device = input_ids.device if input_ids is not None else inputs_embeds.device
@@ -876,22 +975,23 @@ def forward(
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
-        # embed positions
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
-            )
-            padding_mask = None
+
+        if getattr(self.config, "_flash_attn_2_enabled", False):
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
         else:
-            if 0 in attention_mask:
-                padding_mask = attention_mask
+            key_value_length = seq_length + past_key_values_length
+            # 4d mask is passed through the layers
+            if attention_mask is not None:
+                attention_mask = self.attn_mask_converter.to_4d(
+                    attention_mask, seq_length, key_value_length, dtype=inputs_embeds.dtype
+                )
             else:
-                padding_mask = None
-
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-        )
+                attention_mask = self.attn_mask_converter.to_causal_4d(
+                    batch_size, seq_length, key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+                )
 
+        # embed positions
         hidden_states = inputs_embeds
 
         if self.gradient_checkpointing and self.training:
@@ -917,7 +1017,7 @@ def forward(
                 def create_custom_forward(module):
                     def custom_forward(*inputs):
                         # None for past_key_value
-                        return module(*inputs, past_key_value, output_attentions, padding_mask=padding_mask)
+                        return module(*inputs, past_key_value, output_attentions)
 
                     return custom_forward
 
@@ -932,7 +1032,6 @@ def custom_forward(*inputs):
                     past_key_value=past_key_value,
                     output_attentions=output_attentions,
                     use_cache=use_cache,
-                    padding_mask=padding_mask,
                 )
 
             hidden_states = layer_outputs[0]
diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
index d650d60b8a553e..f735c471268136 100644
--- a/src/transformers/models/mistral/modeling_mistral.py
+++ b/src/transformers/models/mistral/modeling_mistral.py
@@ -20,6 +20,7 @@
 """ PyTorch Mistral model."""
 import inspect
 import math
+import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -53,10 +54,147 @@
 _CONFIG_FOR_DOC = "MistralConfig"
 
 
+# Copied from transformers.models.llama.modeling_llama.AttnMaskConverter
+class AttnMaskConverter:
+    """
+    A utility attention mask class that allows:
+        - Create a causal 4d mask
+        - Create a causal 4d mask with slided window
+        - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
+          key_value_length) that can be multiplied with attention scores
+
+    Parameters:
+        is_causal (`bool`):
+            Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
+
+        sliding_window (`int`, *optional*):
+            Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
+    """
+
+    def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
+        self.is_causal = is_causal
+        self.sliding_window = sliding_window
+
+    def to_causal_4d(
+        self,
+        batch_size: int,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype = torch.float32,
+        device: Union[torch.device, "str"] = "cpu",
+    ) -> torch.Tensor:
+        """
+        Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
+        bias to upper right hand triangular matrix (causal mask).
+        """
+        if not self.is_causal:
+            raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
+
+        # If shape is not cached, create a new causal mask and cache it
+        input_shape = (batch_size, query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if input_shape[-1] > 1 or self.sliding_window is not None:
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+
+        return causal_4d_mask
+
+    def to_4d(
+        self,
+        attention_mask_2d: torch.Tensor,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype = torch.float32,
+    ) -> torch.Tensor:
+        """
+        Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
+        key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
+        causal, a causal mask will be added.
+        """
+        input_shape = (attention_mask_2d.shape[0], query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=attention_mask_2d.device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+        elif self.sliding_window is not None:
+            raise NotImplementedError("Sliding window is currently only implemented for causal masking")
+
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
+            attention_mask_2d.device
+        )
+        expanded_4d_mask = expanded_attn_mask if causal_4d_mask is None else expanded_attn_mask + causal_4d_mask
+
+        return expanded_4d_mask
+
+    def _make_causal_mask(
+        self,
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+        sliding_window: Optional[int] = None,
+    ):
+        """
+        Make causal mask used for bi-directional self-attention.
+        """
+        bsz, tgt_len = input_ids_shape
+        mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+        mask_cond = torch.arange(mask.size(-1), device=device)
+        mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+
+        mask = mask.to(dtype)
+
+        if past_key_values_length > 0:
+            mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+
+        # add lower triangular sliding window mask if necessary
+        if sliding_window is not None:
+            diagonal = past_key_values_length - sliding_window + 1
+
+            context_mask = 1 - torch.triu(torch.ones_like(mask, dtype=torch.int), diagonal=diagonal)
+            mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min)
+
+        return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+    def _expand_mask(self, mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+        """
+        Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+        """
+        bsz, src_len = mask.size()
+        tgt_len = tgt_len if tgt_len is not None else src_len
+
+        expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+        inverted_mask = 1.0 - expanded_mask
+
+        return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
 # Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(padding_mask):
-    seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten()
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
     return (
@@ -66,33 +204,6 @@ def _get_unpad_data(padding_mask):
     )
 
 
-def _make_sliding_window_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-    sliding_window: int = 4096,
-):
-    """
-    Make causal mask used for sliding window attention
-    """
-    bsz, tgt_len = input_ids_shape
-
-    tensor = torch.full(
-        (tgt_len, tgt_len),
-        fill_value=1,
-        device=device,
-    )
-    mask = torch.tril(tensor, diagonal=0)
-    # make the mask banded to account for sliding window
-    mask = torch.triu(mask, diagonal=-sliding_window)
-    mask = torch.log(mask).to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
 # Copied from transformers.models.bart.modeling_bart._expand_mask
 def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
     """
@@ -223,6 +334,7 @@ def __init__(self, config: MistralConfig):
         self.num_key_value_groups = self.num_heads // self.num_key_value_heads
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
+        self.is_causal = True
 
         if (self.head_dim * self.num_heads) != self.hidden_size:
             raise ValueError(
@@ -251,8 +363,12 @@ def forward(
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        padding_mask: Optional[torch.Tensor] = None,
+        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
         bsz, q_len, _ = hidden_states.size()
 
         query_states = self.q_proj(hidden_states)
@@ -332,8 +448,15 @@ def forward(
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        padding_mask: Optional[torch.LongTensor] = None,
+        **kwargs,
     ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
         bsz, q_len, _ = hidden_states.size()
 
         query_states = self.q_proj(hidden_states)
@@ -385,9 +508,9 @@ def forward(
 
                 past_key_value = (past_key, past_value)
 
-                if padding_mask is not None:
-                    padding_mask = padding_mask[:, slicing_tokens:]
-                    padding_mask = torch.cat([padding_mask, torch.ones_like(padding_mask[:, -1:])], dim=-1)
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
 
             key_states = torch.cat([past_key_value[0], key_states], dim=2)
             value_states = torch.cat([past_key_value[1], value_states], dim=2)
@@ -433,7 +556,7 @@ def forward(
             query_states,
             key_states,
             value_states,
-            padding_mask,
+            attention_mask,
             q_len,
             dropout=dropout_rate,
             use_sliding_windows=use_sliding_windows,
@@ -452,7 +575,7 @@ def _flash_attention_forward(
         query_states,
         key_states,
         value_states,
-        padding_mask,
+        attention_mask,
         query_length,
         dropout=0.0,
         softmax_scale=None,
@@ -469,7 +592,7 @@ def _flash_attention_forward(
                 Input key states to be passed to Flash Attention API
             value_states (`torch.Tensor`):
                 Input value states to be passed to Flash Attention API
-            padding_mask (`torch.Tensor`):
+            attention_mask (`torch.Tensor`):
                 The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                 position of padding tokens and 1 for the position of non-padding tokens.
             dropout (`int`, *optional*):
@@ -480,10 +603,10 @@ def _flash_attention_forward(
                 Whether to activate sliding window attention.
         """
         # Contains at least one padding token in the sequence
-        if padding_mask is not None:
+        if attention_mask is not None:
             batch_size = query_states.shape[0]
             query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, padding_mask, query_length
+                query_states, key_states, value_states, attention_mask, query_length
             )
 
             cu_seqlens_q, cu_seqlens_k = cu_seq_lens
@@ -500,7 +623,7 @@ def _flash_attention_forward(
                     max_seqlen_k=max_seqlen_in_batch_k,
                     dropout_p=dropout,
                     softmax_scale=softmax_scale,
-                    causal=True,
+                    causal=self.is_causal,
                 )
             else:
                 attn_output_unpad = flash_attn_varlen_func(
@@ -513,7 +636,7 @@ def _flash_attention_forward(
                     max_seqlen_k=max_seqlen_in_batch_k,
                     dropout_p=dropout,
                     softmax_scale=softmax_scale,
-                    causal=True,
+                    causal=self.is_causal,
                     window_size=(self.config.sliding_window, self.config.sliding_window),
                 )
 
@@ -536,16 +659,16 @@ def _flash_attention_forward(
 
         return attn_output
 
-    def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length):
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
         batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
 
         # On the first iteration we need to properly re-create the padding mask
         # by slicing it on the proper place
-        if kv_seq_len != padding_mask.shape[-1]:
-            padding_mask_num_tokens = padding_mask.shape[-1]
-            padding_mask = padding_mask[:, padding_mask_num_tokens - kv_seq_len :]
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
 
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
 
         key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
         value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
@@ -566,8 +689,8 @@ def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_l
             query_layer = query_layer.squeeze(1)
         else:
             # The -q_len: slice assumes left padding.
-            padding_mask = padding_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask)
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
 
         return (
             query_layer,
@@ -600,13 +723,17 @@ def forward(
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
-        padding_mask: Optional[torch.Tensor] = None,
+        **kwargs,
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+                `(batch, sequence_length)` where padding elements are indicated by 0.
             output_attentions (`bool`, *optional*):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
@@ -628,7 +755,6 @@ def forward(
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
-            padding_mask=padding_mask,
         )
         hidden_states = residual + hidden_states
 
@@ -775,6 +901,10 @@ def __init__(self, config: MistralConfig):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
+        # create attention mask cache that trickles down to each attention layer
+        # so that the attention_mask cache can be shared among layers
+        self.attn_mask_converter = AttnMaskConverter(is_causal=True, sliding_window=config.sliding_window)
+
         self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         self.layers = nn.ModuleList([MistralDecoderLayer(config) for _ in range(config.num_hidden_layers)])
         self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -789,32 +919,6 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    def _prepare_decoder_attention_mask(
-        self, attention_mask, input_shape, inputs_embeds, past_key_values_length, sliding_window
-    ):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_sliding_window_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-                sliding_window=sliding_window,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
     @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
     def forward(
         self,
@@ -865,23 +969,13 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        padding_mask = None
-
-        # embed positions
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
-            )
-        elif 0 in attention_mask:
-            padding_mask = attention_mask
-
         if (
-            padding_mask is not None
+            attention_mask is not None
             and hasattr(self.config, "_flash_attn_2_enabled")
             and self.config._flash_attn_2_enabled
             and past_key_values is not None
         ):
-            is_padding_right = padding_mask[:, -1].sum().item() != batch_size
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
             if is_padding_right:
                 raise ValueError(
                     "You are attempting to perform batched generation with padding_side='right'"
@@ -889,13 +983,20 @@ def forward(
                     " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                 )
 
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-            sliding_window=self.config.sliding_window,
-        )
+        if getattr(self.config, "_flash_attn_2_enabled", False):
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            key_value_length = seq_length + past_key_values_length
+            # 4d mask is passed through the layers
+            if attention_mask is not None:
+                attention_mask = self.attn_mask_converter.to_4d(
+                    attention_mask, seq_length, key_value_length, dtype=inputs_embeds.dtype
+                )
+            else:
+                attention_mask = self.attn_mask_converter.to_causal_4d(
+                    batch_size, seq_length, key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+                )
 
         hidden_states = inputs_embeds
 
@@ -922,7 +1023,7 @@ def forward(
                 def create_custom_forward(module):
                     def custom_forward(*inputs):
                         # None for past_key_value
-                        return module(*inputs, past_key_value, output_attentions, padding_mask=padding_mask)
+                        return module(*inputs, past_key_value, output_attentions)
 
                     return custom_forward
 
@@ -940,7 +1041,6 @@ def custom_forward(*inputs):
                     past_key_value=past_key_value,
                     output_attentions=output_attentions,
                     use_cache=use_cache,
-                    padding_mask=padding_mask,
                 )
 
             hidden_states = layer_outputs[0]
diff --git a/src/transformers/models/persimmon/modeling_persimmon.py b/src/transformers/models/persimmon/modeling_persimmon.py
index a0bc5726382336..d73cc4484484fa 100644
--- a/src/transformers/models/persimmon/modeling_persimmon.py
+++ b/src/transformers/models/persimmon/modeling_persimmon.py
@@ -548,7 +548,6 @@ class PersimmonModel(PersimmonPreTrainedModel):
         config: PersimmonConfig
     """
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaModel.__init__ with LLAMA->PERSIMMON,Llama->Persimmon,PersimmonRMSNorm->nn.LayerNorm,norm->final_layernorm,rms_final_layernorm_eps->layer_norm_eps
     def __init__(self, config: PersimmonConfig):
         super().__init__(config)
         self.padding_idx = config.pad_token_id
diff --git a/tests/models/llama/test_modeling_llama.py b/tests/models/llama/test_modeling_llama.py
index 2402986900fda6..df41c6c5f52044 100644
--- a/tests/models/llama/test_modeling_llama.py
+++ b/tests/models/llama/test_modeling_llama.py
@@ -39,6 +39,149 @@
         LlamaModel,
         LlamaTokenizer,
     )
+    from transformers.models.llama.modeling_llama import AttnMaskConverter
+
+
+@require_torch
+class AttentionMaskTester(unittest.TestCase):
+    def check_non_causal(self, bsz, q_len, kv_len, mask_2d, mask_4d):
+        mask_indices = (mask_2d != 1)[:, None].broadcast_to((bsz, q_len, kv_len))
+        mask_4d_values = mask_4d[:, 0][mask_indices]
+        is_inf = mask_4d_values == -float("inf")
+        is_min = mask_4d_values == torch.finfo(mask_4d.dtype).min
+        assert torch.logical_or(is_inf, is_min).all()
+
+    def check_to_4d(self, mask_converter, q_len, kv_len, additional_mask=None, bsz=3):
+        mask_2d = torch.ones((bsz, kv_len), device=torch_device, dtype=torch.long)
+
+        if additional_mask is not None:
+            for bsz_idx, seq_idx in additional_mask:
+                mask_2d[bsz_idx, seq_idx] = 0
+
+        mask_4d = mask_converter.to_4d(mask_2d, query_length=q_len, key_value_length=kv_len)
+
+        assert mask_4d.shape == (bsz, 1, q_len, kv_len)
+
+        context = mask_converter.sliding_window
+        if mask_converter.is_causal and context is None:
+            # k * (k+1) / 2 tokens are masked in triangualar masks
+            num_tokens_masked = bsz * (q_len * (q_len - 1) // 2)
+
+            if 0 not in mask_2d:
+                assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
+            if 0 in mask_2d:
+                # at least causal mask + maybe more
+                assert (mask_4d != 0).sum().cpu().item() >= num_tokens_masked
+                self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d)
+        elif not mask_converter.is_causal and context is None:
+            if 0 not in mask_2d:
+                assert (mask_4d != 0).sum().cpu().item() == 0
+            if 0 in mask_2d:
+                self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d)
+        elif mask_converter.is_causal and context is not None:
+            # k * (k+1) / 2 tokens are masked in triangualar masks
+            num_tokens_masked = (q_len * (q_len - 1) // 2) + self.compute_num_context_mask(kv_len, context, q_len)
+            num_tokens_masked = bsz * num_tokens_masked
+
+            if 0 not in mask_2d:
+                assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
+            if 0 in mask_2d:
+                # at least causal mask + maybe more
+                assert (mask_4d != 0).sum().cpu().item() >= num_tokens_masked
+                self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d)
+
+    def check_to_causal(self, mask_converter, q_len, kv_len, bsz=3):
+        mask_4d = mask_converter.to_causal_4d(bsz, query_length=q_len, key_value_length=kv_len, device=torch_device)
+
+        if q_len == 1 and mask_converter.sliding_window is None:
+            # no causal mask if q_len is 1
+            assert mask_4d is None
+            return
+
+        context = mask_converter.sliding_window
+        if mask_converter.is_causal and context is None:
+            # k * (k+1) / 2 tokens are masked in triangualar masks
+            num_tokens_masked = bsz * (q_len * (q_len - 1) // 2)
+
+            assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
+        elif not mask_converter.is_causal and context is None:
+            assert (mask_4d != 0).sum().cpu().item() == 0
+        elif mask_converter.is_causal and context is not None:
+            # k * (k+1) / 2 tokens are masked in triangualar masks
+            num_tokens_masked = (q_len * (q_len - 1) // 2) + self.compute_num_context_mask(kv_len, context, q_len)
+            num_tokens_masked = bsz * num_tokens_masked
+
+            assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
+
+    def compute_num_context_mask(self, kv_len, context, q_len):
+        # This function computes the # of attention tokens that are added for
+        # the sliding window
+        c_mask_len = kv_len - context
+        num_mask_triangle = c_mask_len * (c_mask_len + 1) // 2
+        cut_mask_len = max(c_mask_len - q_len, 0)
+        num_cut_mask = cut_mask_len * (cut_mask_len + 1) // 2
+        return num_mask_triangle - num_cut_mask
+
+    def test_2d_to_4d_causal(self):
+        mask_converter = AttnMaskConverter(is_causal=True)
+
+        # auto-regressive use case
+        self.check_to_4d(mask_converter, q_len=1, kv_len=7)
+        # special auto-regressive case
+        self.check_to_4d(mask_converter, q_len=3, kv_len=7)
+        # non auto-regressive case
+        self.check_to_4d(mask_converter, q_len=7, kv_len=7)
+
+        # same with extra attention masks
+        self.check_to_4d(mask_converter, q_len=1, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+        self.check_to_4d(mask_converter, q_len=3, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+        self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+
+    def test_2d_to_4d(self):
+        torch.ones((3, 7), device=torch_device, dtype=torch.long)
+        mask_converter = AttnMaskConverter(is_causal=False)
+
+        # non auto-regressive case
+        self.check_to_4d(mask_converter, q_len=7, kv_len=7)
+
+        # same with extra attention masks
+        self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+
+    def test_2d_to_4d_causal_sliding(self):
+        torch.ones((3, 7), device=torch_device, dtype=torch.long)
+        mask_converter = AttnMaskConverter(is_causal=True, sliding_window=5)
+
+        # auto-regressive use case
+        self.check_to_4d(mask_converter, q_len=1, kv_len=7)
+        # special auto-regressive case
+        self.check_to_4d(mask_converter, q_len=3, kv_len=7)
+        # non auto-regressive case
+        self.check_to_4d(mask_converter, q_len=7, kv_len=7)
+
+        # same with extra attention masks
+        self.check_to_4d(mask_converter, q_len=1, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+        self.check_to_4d(mask_converter, q_len=3, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+        self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
+
+    def test_causal_mask(self):
+        mask_converter = AttnMaskConverter(is_causal=True)
+
+        # auto-regressive use case
+        self.check_to_causal(mask_converter, q_len=1, kv_len=7)
+        # special auto-regressive case
+        self.check_to_causal(mask_converter, q_len=3, kv_len=7)
+        # non auto-regressive case
+        self.check_to_causal(mask_converter, q_len=7, kv_len=7)
+
+    def test_causal_mask_sliding(self):
+        mask_converter = AttnMaskConverter(is_causal=True, sliding_window=3)
+
+        # auto-regressive use case
+        self.check_to_causal(mask_converter, q_len=1, kv_len=7)
+        # special auto-regressive case
+        self.check_to_causal(mask_converter, q_len=3, kv_len=7)
+        # non auto-regressive case
+        self.check_to_causal(mask_converter, q_len=7, kv_len=7)
 
 
 class LlamaModelTester: