Update modeling_llama.py

updating based on transformers==4.49

modeling_llama.py

@@ -17,162 +17,68 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""PyTorch LLaMA model."""
+
 import math
+import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
-from transformers.models.llama.modeling_llama import LlamaRMSNorm
 from transformers.activations import ACT2FN
 from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.generation import GenerationMixin
 from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs, _flash_attention_forward
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
     QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
 )
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
 from transformers.modeling_utils import PreTrainedModel
-from transformers.processing_utils import Unpack
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.utils import (
-    LossKwargs,
-    add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
     logging,
     replace_return_docstrings,
 )
 from .configuration_llama import LlamaConfig
-from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "meta-llama/Llama-2-7b-hf"
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
-
-
-class LlamaRotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim=None,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scaling_factor=1.0,
-        rope_type="default",
-        config: Optional[LlamaConfig] = None,
-    ):
-        super().__init__()
-        # TODO (joao): remove the `if` below, only used for BC
-        self.rope_kwargs = {}
-        if config is None:
-            logger.warning_once(
-                "`LlamaRotaryEmbedding` can now be fully parameterized by passing the model config through the "
-                "`config` argument. All other arguments will be removed in v4.46"
-            )
-            self.rope_kwargs = {
-                "rope_type": rope_type,
-                "factor": scaling_factor,
-                "dim": dim,
-                "base": base,
-                "max_position_embeddings": max_position_embeddings,
-            }
-            self.rope_type = rope_type
-            self.max_seq_len_cached = max_position_embeddings
-            self.original_max_seq_len = max_position_embeddings
-        else:
-            # BC: "rope_type" was originally "type"
-            if config.rope_scaling is not None:
-                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-            else:
-                self.rope_type = "default"
-            self.max_seq_len_cached = config.max_position_embeddings
-            self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    def _dynamic_frequency_update(self, position_ids, device):
-        """
-        dynamic RoPE layers should recompute `inv_freq` in the following situations:
-        1 - growing beyond the cached sequence length (allow scaling)
-        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
-        """
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.max_seq_len_cached:  # growth
-            inv_freq, self.attention_scaling = self.rope_init_fn(
-                self.config, device, seq_len=seq_len, **self.rope_kwargs
-            )
-            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
-            self.max_seq_len_cached = seq_len
-
-        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
-            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
-            self.max_seq_len_cached = self.original_max_seq_len
+from transformers.models.llama.modeling_llama import LlamaRMSNorm
+from transformers.models.llama.modeling_llama import (
+    apply_rotary_pos_emb,
+    LlamaRotaryEmbedding,
+)
 
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        if "dynamic" in self.rope_type:
-            self._dynamic_frequency_update(position_ids, device=x.device)
 
-        # Core RoPE block
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
 
-        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
-        cos = cos * self.attention_scaling
-        sin = sin * self.attention_scaling
 
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+logger = logging.get_logger(__name__)
 
+_CONFIG_FOR_DOC = "LlamaConfig"
 
-class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
-    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
-
-    def __init__(self, *args, **kwargs):
-        logger.warning_once(
-            "`LlamaLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use "
-            "`LlamaRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)."
-        )
-        kwargs["rope_type"] = "linear"
-        super().__init__(*args, **kwargs)
 
+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.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
 
-class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
-    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 
-    def __init__(self, *args, **kwargs):
-        logger.warning_once(
-            "`LlamaDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use "
-            "`LlamaRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to "
-            "__init__)."
-        )
-        kwargs["rope_type"] = "dynamic"
-        super().__init__(*args, **kwargs)
+ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
 
 
 class LlamaMLP(nn.Module):
@@ -181,9 +87,9 @@ class LlamaMLP(nn.Module):
         self.config = config
         self.hidden_size = config.hidden_size
         self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
         self.act_fn = ACT2FN[config.hidden_act]
 
     def forward(self, x):
@@ -194,13 +100,24 @@ class LlamaMLP(nn.Module):
             down_proj_slices = self.down_proj.weight.split(slice, dim=1)
 
             gate_proj = torch.cat(
-                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+                [
+                    F.linear(x, gate_proj_slices[i])
+                    for i in range(self.config.pretraining_tp)
+                ],
+                dim=-1,
+            )
+            up_proj = torch.cat(
+                [
+                    F.linear(x, up_proj_slices[i])
+                    for i in range(self.config.pretraining_tp)
+                ],
+                dim=-1,
             )
-            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
 
             intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
             down_proj = [
-                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+                F.linear(intermediate_states[i], down_proj_slices[i])
+                for i in range(self.config.pretraining_tp)
             ]
             down_proj = sum(down_proj)
         else:
@@ -217,7 +134,9 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     batch, num_key_value_heads, slen, head_dim = hidden_states.shape
     if n_rep == 1:
         return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
     return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
 
 
@@ -238,20 +157,59 @@ class LlamaAttention(nn.Module):
         self.attention_dropout = config.attention_dropout
         self.hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
-        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+        self.head_dim = self.hidden_size // self.num_heads
         self.num_key_value_heads = config.num_key_value_heads
         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
 
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(
+            self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            self.hidden_size, self.hidden_size, bias=config.attention_bias
+        )
+        self._init_rope()
 
-        # TODO (joao): remove in v4.46 (RoPE is computed in the model, not in the decoder layers)
-        self.rotary_emb = LlamaRotaryEmbedding(config=self.config)
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(self.config)
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def forward(
         self,
@@ -262,26 +220,36 @@ class LlamaAttention(nn.Module):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
         if self.config.pretraining_tp > 1:
-            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            key_value_slicing = (
+                self.num_key_value_heads * self.head_dim
+            ) // self.config.pretraining_tp
             query_slices = self.q_proj.weight.split(
                 (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
             )
             key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
             value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
 
-            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = [
+                F.linear(hidden_states, query_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
             query_states = torch.cat(query_states, dim=-1)
 
-            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = [
+                F.linear(hidden_states, key_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
             key_states = torch.cat(key_states, dim=-1)
 
-            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = [
+                F.linear(hidden_states, value_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
             value_states = torch.cat(value_states, dim=-1)
 
         else:
@@ -289,38 +257,47 @@ class LlamaAttention(nn.Module):
             key_states = self.k_proj(hidden_states)
             value_states = self.v_proj(hidden_states)
 
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        if position_embeddings is None:
-            logger.warning_once(
-                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
-                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
-                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
-                "removed and `position_embeddings` will be mandatory."
-            )
-            cos, sin = self.rotary_emb(value_states, position_ids)
-        else:
-            cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
 
         if past_key_value is not None:
             # sin and cos are specific to RoPE models; cache_position needed for the static cache
             cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
 
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
 
         if attention_mask is not None:  # no matter the length, we just slice it
             causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
             attn_weights = attn_weights + causal_mask
 
         # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
         attn_output = torch.matmul(attn_weights, value_states)
 
         if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
@@ -331,12 +308,21 @@ class LlamaAttention(nn.Module):
 
         attn_output = attn_output.transpose(1, 2).contiguous()
 
-        attn_output = attn_output.reshape(bsz, q_len, -1)
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
         if self.config.pretraining_tp > 1:
-            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
-            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
-            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+            attn_output = attn_output.split(
+                self.hidden_size // self.config.pretraining_tp, dim=2
+            )
+            o_proj_slices = self.o_proj.weight.split(
+                self.hidden_size // self.config.pretraining_tp, dim=1
+            )
+            attn_output = sum(
+                [
+                    F.linear(attn_output[i], o_proj_slices[i])
+                    for i in range(self.config.pretraining_tp)
+                ]
+            )
         else:
             attn_output = self.o_proj(attn_output)
 
@@ -370,15 +356,8 @@ class LlamaFlashAttention2(LlamaAttention):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
-        **kwargs: Unpack[FlashAttentionKwargs],
+        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if isinstance(past_key_value, StaticCache):
-            raise ValueError(
-                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
-                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
-            )
-
         output_attentions = False
 
         bsz, q_len, _ = hidden_states.size()
@@ -390,26 +369,29 @@ class LlamaFlashAttention2(LlamaAttention):
         # Flash attention requires the input to have the shape
         # batch_size x seq_length x head_dim x hidden_dim
         # therefore we just need to keep the original shape
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
 
-        if position_embeddings is None:
-            logger.warning_once(
-                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
-                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
-                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
-                "removed and `position_embeddings` will be mandatory."
-            )
-            cos, sin = self.rotary_emb(value_states, position_ids)
-        else:
-            cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        past_key_value = getattr(self, "past_key_value", past_key_value)
 
         if past_key_value is not None:
             # sin and cos are specific to RoPE models; cache_position needed for the static cache
             cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
 
         # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
         # to be able to avoid many of these transpose/reshape/view.
@@ -445,21 +427,16 @@ class LlamaFlashAttention2(LlamaAttention):
             key_states = key_states.to(target_dtype)
             value_states = value_states.to(target_dtype)
 
-        attn_output = _flash_attention_forward(
+        attn_output = self._flash_attention_forward(
             query_states,
             key_states,
             value_states,
             attention_mask,
             q_len,
-            position_ids=position_ids,
             dropout=dropout_rate,
-            sliding_window=getattr(self, "sliding_window", None),
-            use_top_left_mask=self._flash_attn_uses_top_left_mask,
-            is_causal=self.is_causal,
-            **kwargs,
         )
 
-        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -467,6 +444,131 @@ class LlamaFlashAttention2(LlamaAttention):
 
         return attn_output, attn_weights, past_key_value
 
+    def _flash_attention_forward(
+        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
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            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 (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        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, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(
+                attn_output_unpad, indices_q, batch_size, query_length
+            )
+        else:
+            attn_output = flash_attn_func(
+                query_states,
+                key_states,
+                value_states,
+                dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+        return attn_output
+
+    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(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
+                indices_k,
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            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,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
 
 class LlamaSdpaAttention(LlamaAttention):
     """
@@ -485,8 +587,6 @@ class LlamaSdpaAttention(LlamaAttention):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         if output_attentions:
             # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
@@ -502,7 +602,6 @@ class LlamaSdpaAttention(LlamaAttention):
                 output_attentions=output_attentions,
                 use_cache=use_cache,
                 cache_position=cache_position,
-                position_embeddings=position_embeddings,
             )
 
         bsz, q_len, _ = hidden_states.size()
@@ -511,26 +610,30 @@ class LlamaSdpaAttention(LlamaAttention):
         key_states = self.k_proj(hidden_states)
         value_states = self.v_proj(hidden_states)
 
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
 
-        if position_embeddings is None:
-            logger.warning_once(
-                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
-                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
-                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
-                "removed and `position_embeddings` will be mandatory."
-            )
-            cos, sin = self.rotary_emb(value_states, position_ids)
-        else:
-            cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        # In case static cache is used, it is an instance attribute.
+        past_key_value = getattr(self, "past_key_value", past_key_value)
 
         if past_key_value is not None:
             # sin and cos are specific to RoPE models; cache_position needed for the static cache
             cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
 
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
@@ -546,21 +649,19 @@ class LlamaSdpaAttention(LlamaAttention):
             key_states = key_states.contiguous()
             value_states = value_states.contiguous()
 
-        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
-        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
-        is_causal = True if causal_mask is None and q_len > 1 else False
-
+        # In case we are not compiling, we may set `causal_mask` to None, which is required to dispatch to SDPA's Flash Attention 2 backend, rather
+        # relying on the `is_causal` argument.
         attn_output = torch.nn.functional.scaled_dot_product_attention(
             query_states,
             key_states,
             value_states,
             attn_mask=causal_mask,
             dropout_p=self.attention_dropout if self.training else 0.0,
-            is_causal=is_causal,
+            is_causal=causal_mask is None and q_len > 1,
         )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, -1)
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
 
         attn_output = self.o_proj(attn_output)
 
@@ -579,24 +680,29 @@ class LlamaDecoderLayer(nn.Module):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
+            config=config, layer_idx=layer_idx
+        )
 
         self.mlp = LlamaMLP(config)
         self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
 
     def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
         **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> 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)`
@@ -610,15 +716,12 @@ class LlamaDecoderLayer(nn.Module):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
             past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-                Indices depicting the position of the input sequence tokens in the sequence
-            position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
-                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
-                with `head_dim` being the embedding dimension of each attention head.
-            kwargs (`dict`, *optional*):
-                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
-                into the model
         """
+        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
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -632,7 +735,6 @@ class LlamaDecoderLayer(nn.Module):
             output_attentions=output_attentions,
             use_cache=use_cache,
             cache_position=cache_position,
-            position_embeddings=position_embeddings,
             **kwargs,
         )
         hidden_states = residual + hidden_states
@@ -684,8 +786,6 @@ class LlamaPreTrainedModel(PreTrainedModel):
     _supports_flash_attn_2 = True
     _supports_sdpa = True
     _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
 
     def _init_weights(self, module):
         std = self.config.initializer_range
@@ -698,6 +798,32 @@ class LlamaPreTrainedModel(PreTrainedModel):
             if module.padding_idx is not None:
                 module.weight.data[module.padding_idx].zero_()
 
+    def _setup_cache(
+        self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None
+    ):
+        if (
+            self.config._attn_implementation == "flash_attention_2"
+            and cache_cls == StaticCache
+        ):
+            raise ValueError(
+                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+            )
+
+        for layer in self.model.layers:
+            device = layer.input_layernorm.weight.device
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                dtype = self.config._pre_quantization_dtype
+            else:
+                dtype = layer.self_attn.o_proj.weight.dtype
+            layer.self_attn.past_key_value = cache_cls(
+                self.config, max_batch_size, max_cache_len, device=device, dtype=dtype
+            )
+
+    def _reset_cache(self):
+        for layer in self.model.layers:
+            layer.self_attn.past_key_value = None
+
 
 LLAMA_INPUTS_DOCSTRING = r"""
     Args:
@@ -740,8 +866,7 @@ LLAMA_INPUTS_DOCSTRING = r"""
             returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
 
             Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance, see our
-            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
+            - a [`~cache_utils.Cache`] instance;
             - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
             shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
             cache format.
@@ -791,12 +916,16 @@ class LlamaModel(LlamaPreTrainedModel):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
         self.layers = nn.ModuleList(
-            [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+            [
+                LlamaDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
         )
         self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = LlamaRotaryEmbedding(config=config)
         self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
@@ -814,24 +943,33 @@ class LlamaModel(LlamaPreTrainedModel):
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
     ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        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
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(
@@ -842,35 +980,35 @@ class LlamaModel(LlamaPreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        # kept for BC (non `Cache` `past_key_values` inputs)
-        return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
-            return_legacy_cache = True
-            if past_key_values is None:
-                past_key_values = DynamicCache()
-            else:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-                logger.warning_once(
-                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
-                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
-                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
-                )
+        past_seen_tokens = 0
+        if use_cache:  # kept for BC (cache positions)
+            if past_key_values is not None and not isinstance(
+                past_key_values, StaticCache
+            ):
+                if not isinstance(past_key_values, DynamicCache):
+                    past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                past_seen_tokens = past_key_values.get_seq_length()
 
         if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            if isinstance(past_key_values, StaticCache):
+                raise ValueError(
+                    "cache_position is a required argument when using StaticCache."
+                )
             cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
             )
+
         if position_ids is None:
             position_ids = cache_position.unsqueeze(0)
 
         causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+            attention_mask, inputs_embeds, cache_position, past_seen_tokens
         )
-        hidden_states = inputs_embeds
 
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        # embed positions
+        hidden_states = inputs_embeds
 
         # decoder layers
         all_hidden_states = () if output_hidden_states else None
@@ -891,7 +1029,6 @@ class LlamaModel(LlamaPreTrainedModel):
                     output_attentions,
                     use_cache,
                     cache_position,
-                    position_embeddings,
                 )
             else:
                 layer_outputs = decoder_layer(
@@ -902,8 +1039,6 @@ class LlamaModel(LlamaPreTrainedModel):
                     output_attentions=output_attentions,
                     use_cache=use_cache,
                     cache_position=cache_position,
-                    position_embeddings=position_embeddings,
-                    **flash_attn_kwargs,
                 )
 
             hidden_states = layer_outputs[0]
@@ -920,12 +1055,15 @@ class LlamaModel(LlamaPreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = next_decoder_cache if use_cache else None
-        if return_legacy_cache:
-            next_cache = next_cache.to_legacy_cache()
-
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache
         if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=next_cache,
@@ -938,127 +1076,100 @@ class LlamaModel(LlamaPreTrainedModel):
         attention_mask: torch.Tensor,
         input_tensor: torch.Tensor,
         cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
+        past_seen_tokens: int,
     ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
         if self.config._attn_implementation == "flash_attention_2":
             if attention_mask is not None and 0.0 in attention_mask:
                 return attention_mask
             return None
 
-        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
-        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
-        # to infer the attention mask.
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+        if self.config._attn_implementation == "sdpa":
+            # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
+            # in order to dispatch on Flash Attention 2.
             if AttentionMaskConverter._ignore_causal_mask_sdpa(
                 attention_mask,
                 inputs_embeds=input_tensor,
                 past_key_values_length=past_seen_tokens,
-                is_training=self.training,
             ):
                 return None
 
         dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
-        if using_static_cache:
-            target_length = past_key_values.get_max_cache_shape()
-        else:
+        if hasattr(
+            getattr(self.layers[0], "self_attn", {}), "past_key_value"
+        ):  # static cache
+            target_length = self.config.max_position_embeddings
+        else:  # dynamic cache
             target_length = (
                 attention_mask.shape[-1]
                 if isinstance(attention_mask, torch.Tensor)
                 else past_seen_tokens + sequence_length + 1
             )
 
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
+        causal_mask = torch.full(
+            (sequence_length, target_length),
+            fill_value=min_dtype,
             dtype=dtype,
             device=device,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
         )
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(
+            target_length, device=device
+        ) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(
+            input_tensor.shape[0], 1, -1, -1
+        )
+        if attention_mask is not None:
+            causal_mask = (
+                causal_mask.clone()
+            )  # copy to contiguous memory for in-place edit
+            if attention_mask.dim() == 2:
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[
+                    :, None, None, :
+                ].eq(0.0)
+                causal_mask[..., :mask_length] = causal_mask[
+                    ..., :mask_length
+                ].masked_fill(padding_mask, min_dtype)
+            elif attention_mask.dim() == 4:
+                # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
+                # cache. In that case, the 4D attention mask attends to the newest tokens only.
+                if attention_mask.shape[-2] < cache_position[0] + sequence_length:
+                    offset = cache_position[0]
+                else:
+                    offset = 0
+                mask_shape = attention_mask.shape
+                mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
+                causal_mask[
+                    : mask_shape[0],
+                    : mask_shape[1],
+                    offset : mask_shape[2] + offset,
+                    : mask_shape[3],
+                ] = mask_slice
 
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None
             and attention_mask.device.type == "cuda"
-            and not output_attentions
         ):
             # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
             # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
             # Details: https://github.com/pytorch/pytorch/issues/110213
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
-
-        return causal_mask
-
-    @staticmethod
-    def _prepare_4d_causal_attention_mask_with_cache_position(
-        attention_mask: torch.Tensor,
-        sequence_length: int,
-        target_length: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        cache_position: torch.Tensor,
-        batch_size: int,
-        **kwargs,
-    ):
-        """
-        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
-                `(batch_size, 1, query_length, key_value_length)`.
-            sequence_length (`int`):
-                The sequence length being processed.
-            target_length (`int`):
-                The target length: when generating with static cache, the mask should be as long as the static cache,
-                to account for the 0 padding, the part of the cache that is not filled yet.
-            dtype (`torch.dtype`):
-                The dtype to use for the 4D attention mask.
-            device (`torch.device`):
-                The device to plcae the 4D attention mask on.
-            cache_position (`torch.Tensor`):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            batch_size (`torch.Tensor`):
-                Batch size.
-        """
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-            causal_mask = attention_mask
-        else:
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            causal_mask = AttentionMaskConverter._unmask_unattended(
+                causal_mask, min_dtype
             )
-            if sequence_length != 1:
-                causal_mask = torch.triu(causal_mask, diagonal=1)
-            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
 
         return causal_mask
 
 
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
-
-
-class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
+class LlamaForCausalLM(LlamaPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
@@ -1089,13 +1200,15 @@ class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
         return self.model
 
     @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
     def forward(
         self,
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
@@ -1103,8 +1216,6 @@ class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        num_logits_to_keep: int = 0,
-        **kwargs: Unpack[KwargsForCausalLM],
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         r"""
         Args:
@@ -1113,11 +1224,6 @@ class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
                 config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                 (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
 
-            num_logits_to_keep (`int`, *optional*):
-                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
-                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-
         Returns:
 
         Example:
@@ -1136,11 +1242,19 @@ class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
         "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
         ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        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
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
         )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
         outputs = self.model(
@@ -1154,21 +1268,34 @@ class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             cache_position=cache_position,
-            **kwargs,
         )
 
         hidden_states = outputs[0]
         if self.config.pretraining_tp > 1:
-            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
-            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            lm_head_slices = self.lm_head.weight.split(
+                self.vocab_size // self.config.pretraining_tp, dim=0
+            )
+            logits = [
+                F.linear(hidden_states, lm_head_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
             logits = torch.cat(logits, dim=-1)
         else:
-            # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-            logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
 
         loss = None
         if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
 
         if not return_dict:
             output = (logits,) + outputs[1:]
@@ -1182,6 +1309,125 @@ class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
             attentions=outputs.attentions,
         )
 
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        **kwargs,
+    ):
+        # With static cache, the `past_key_values` is None
+        # TODO joao: standardize interface for the different Cache classes and remove of this if
+        has_static_cache = False
+        if past_key_values is None:
+            past_key_values = getattr(
+                getattr(self.model.layers[0], "self_attn", {}), "past_key_value", None
+            )
+            has_static_cache = past_key_values is not None
+
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                past_length = (
+                    cache_position[0]
+                    if cache_position is not None
+                    else past_key_values.get_seq_length()
+                )
+                max_cache_length = (
+                    torch.tensor(
+                        past_key_values.get_max_cache_shape(), device=input_ids.device
+                    )
+                    if past_key_values.get_max_cache_shape() is not None
+                    else None
+                )
+                cache_length = (
+                    past_length
+                    if max_cache_length is None
+                    else torch.min(max_cache_length, past_length)
+                )
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if (
+                attention_mask is not None
+                and attention_mask.shape[1] > input_ids.shape[1]
+            ):
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        input_length = (
+            position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
+        )
+        if cache_position is None:
+            cache_position = torch.arange(
+                past_length, past_length + input_length, device=input_ids.device
+            )
+        else:
+            cache_position = cache_position[-input_length:]
+
+        if has_static_cache:
+            past_key_values = None
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
 
 @add_start_docstrings(
     """
@@ -1217,10 +1463,10 @@ class LlamaForSequenceClassification(LlamaPreTrainedModel):
     @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
     def forward(
         self,
-        input_ids: Optional[torch.LongTensor] = None,
+        input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
@@ -1234,7 +1480,9 @@ class LlamaForSequenceClassification(LlamaPreTrainedModel):
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
         transformer_outputs = self.model(
             input_ids,
@@ -1256,24 +1504,53 @@ class LlamaForSequenceClassification(LlamaPreTrainedModel):
             batch_size = inputs_embeds.shape[0]
 
         if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
         if self.config.pad_token_id is None:
             sequence_lengths = -1
         else:
             if input_ids is not None:
                 # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = (
+                    torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                )
                 sequence_lengths = sequence_lengths % input_ids.shape[-1]
                 sequence_lengths = sequence_lengths.to(logits.device)
             else:
                 sequence_lengths = -1
 
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+        pooled_logits = logits[
+            torch.arange(batch_size, device=logits.device), sequence_lengths
+        ]
 
         loss = None
         if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
         if not return_dict:
             output = (pooled_logits,) + transformer_outputs[1:]
             return ((loss,) + output) if loss is not None else output
@@ -1318,14 +1595,13 @@ class LlamaForQuestionAnswering(LlamaPreTrainedModel):
         input_ids: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         start_positions: Optional[torch.LongTensor] = None,
         end_positions: Optional[torch.LongTensor] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-        **kwargs,
     ) -> Union[Tuple, QuestionAnsweringModelOutput]:
         r"""
         start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -1337,7 +1613,9 @@ class LlamaForQuestionAnswering(LlamaPreTrainedModel):
             Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
             are not taken into account for computing the loss.
         """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
         outputs = self.transformer(
             input_ids,
@@ -1357,106 +1635,31 @@ class LlamaForQuestionAnswering(LlamaPreTrainedModel):
         start_logits = start_logits.squeeze(-1).contiguous()
         end_logits = end_logits.squeeze(-1).contiguous()
 
-        loss = None
+        total_loss = None
         if start_positions is not None and end_positions is not None:
-            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
 
         if not return_dict:
             output = (start_logits, end_logits) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
+            return ((total_loss,) + output) if total_loss is not None else output
 
         return QuestionAnsweringModelOutput(
-            loss=loss,
+            loss=total_loss,
             start_logits=start_logits,
             end_logits=end_logits,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-
-
-@add_start_docstrings(
-    """
-    The Llama Model transformer with a token classification head on top (a linear layer on top of the hidden-states
-    output) e.g. for Named-Entity-Recognition (NER) tasks.
-    """,
-    LLAMA_START_DOCSTRING,
-)
-class LlamaForTokenClassification(LlamaPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = LlamaModel(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )