Update modeling_gemma.py

rebasing on latest transformers

modeling_gemma.py

@@ -1,3 +1,9 @@
+#           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#               This file was automatically generated from <path_to_diff_file.py>.
+#         Do NOT edit this file manually as any edits will be overwritten by the generation of
+#         the file from the diff. If any change should be done, please apply the change to the
+#                                    diff.py file directly.
+#           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # coding=utf-8
 # Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
 #
@@ -13,74 +19,111 @@
 # 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 Gemma 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.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.modeling_attn_mask_utils import (
-    AttentionMaskConverter,
-    _prepare_4d_causal_attention_mask,
-)
-from transformers.modeling_outputs import (
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import _flash_attention_forward
+from ...modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
     SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
 )
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
-from transformers.utils import (
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import ALL_LAYERNORM_LAYERS
+from ...utils import (
     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 transformers.utils.import_utils import is_torch_fx_available
 from .configuration_gemma import GemmaConfig
 
 
-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
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
+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,
+    min_dtype: float,
+    cache_position: torch.Tensor,
+    batch_size: int,
+):
+    """
+    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.
 
-from transformers.models.gemma.modeling_gemma import GemmaRMSNorm
-# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
-# It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
-    if not is_torch_greater_or_equal_than_1_13:
-        import torch.fx
+    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.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        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:
+        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+        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
+            )
 
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+    return causal_mask
 
 
-logger = logging.get_logger(__name__)
+class GemmaRMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
 
-_CONFIG_FOR_DOC = "GemmaConfig"
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
 
+    def forward(self, x):
+        output = self._norm(x.float())
+        # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
+        # See https://github.com/huggingface/transformers/pull/29402
+        output = output * (1.0 + self.weight.float())
+        return output.type_as(x)
 
-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 (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.eps}"
 
 
 ALL_LAYERNORM_LAYERS.append(GemmaRMSNorm)
@@ -93,45 +136,80 @@ class GemmaRotaryEmbedding(nn.Module):
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
-        # self.register_buffer("inv_freq", None, persistent=False)
-        self.inv_freq = None
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
+        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
 
     @torch.no_grad()
     def forward(self, x, position_ids, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        if self.inv_freq is None:
-            self.inv_freq = 1.0 / (
-                self.base
-                ** (
-                    torch.arange(
-                        0, self.dim, 2, dtype=torch.int64, device=x.device
-                    ).float()
-                    / self.dim
-                )
-            )
-        inv_freq_expanded = (
-            self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        )
+        self.inv_freq.to(x.device)
+        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 since bfloat16 loses precision on long contexts
         # 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"
-        )
+        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)
+            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()
         return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 
 
-# Copied from transformers.models.llama.modeling_llama.rotate_half
+class GemmaMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        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=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)
+        if config.hidden_activation is None:
+            logger.warning_once(
+                "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n"
+                "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n"
+                "`config.hidden_activation` if you want to override this behaviour.\n"
+                "See https://github.com/huggingface/transformers/pull/29402 for more details."
+            )
+            config.hidden_activation = "gelu_pytorch_tanh"
+        hidden_activation = config.hidden_activation
+        self.act_fn = ACT2FN[hidden_activation]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class GemmaLinearScalingRotaryEmbedding(GemmaRotaryEmbedding):
+    """GemmaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: a scaling factor is aplied to the position ids
+        position_ids = position_ids.float() / self.scaling_factor
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
+class GemmaDynamicNTKScalingRotaryEmbedding(GemmaRotaryEmbedding):
+    """GemmaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
+
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -139,7 +217,6 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
@@ -167,33 +244,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     return q_embed, k_embed
 
 
-class GemmaMLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        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=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)
-        if config.hidden_activation is None:
-            logger.warning_once(
-                "Gemma's activation function should be approximate GeLU and not exact GeLU.\n"
-                "Changing the activation function to `gelu_pytorch_tanh`."
-                f"if you want to use the legacy `{config.hidden_act}`, "
-                f"edit the `model.config` to set `hidden_activation={config.hidden_act}` "
-                "  instead of `hidden_act`. See https://github.com/huggingface/transformers/pull/29402 for more details."
-            )
-            hidden_activation = "gelu_pytorch_tanh"
-        else:
-            hidden_activation = config.hidden_activation
-        self.act_fn = ACT2FN[hidden_activation]
-
-    def forward(self, x):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     """
     This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
@@ -202,16 +252,13 @@ 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)
 
 
 class GemmaAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
-    # Ignore copy
     def __init__(self, config: GemmaConfig, layer_idx: Optional[int] = None):
         super().__init__()
         self.config = config
@@ -232,6 +279,7 @@ class GemmaAttention(nn.Module):
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.is_causal = True
+        self.scaling = 1 / math.sqrt(config.head_dim)
 
         if self.hidden_size % self.num_heads != 0:
             raise ValueError(
@@ -239,22 +287,10 @@ class GemmaAttention(nn.Module):
                 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.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias
-        )
+        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)
         self.rotary_emb = GemmaRotaryEmbedding(
             self.head_dim,
             max_position_embeddings=self.max_position_embeddings,
@@ -270,7 +306,6 @@ class GemmaAttention(nn.Module):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
@@ -278,47 +313,30 @@ class GemmaAttention(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)
-
-        past_key_value = getattr(self, "past_key_value", past_key_value)
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, None
-        )
+        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 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)) * self.scaling
 
         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):
@@ -338,7 +356,6 @@ class GemmaAttention(nn.Module):
         return attn_output, attn_weights, past_key_value
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Gemma
 class GemmaFlashAttention2(GemmaAttention):
     """
     Gemma flash attention module. This module inherits from `GemmaAttention` as the weights of the module stays
@@ -354,7 +371,6 @@ class GemmaFlashAttention2(GemmaAttention):
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 
-    # Ignore copy
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -364,8 +380,13 @@ class GemmaFlashAttention2(GemmaAttention):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        **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()
@@ -377,29 +398,17 @@ class GemmaFlashAttention2(GemmaAttention):
         # 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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, None
-        )
+        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)
 
         # 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.
@@ -435,13 +444,17 @@ class GemmaFlashAttention2(GemmaAttention):
             key_states = key_states.to(target_dtype)
             value_states = value_states.to(target_dtype)
 
-        attn_output = self._flash_attention_forward(
+        attn_output = _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),
+            is_causal=self.is_causal,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
         )
 
         attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
@@ -452,133 +465,7 @@ class GemmaFlashAttention2(GemmaAttention):
 
         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 GemmaFlashAttention2 __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),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Gemma
 class GemmaSdpaAttention(GemmaAttention):
     """
     Gemma attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
@@ -586,7 +473,7 @@ class GemmaSdpaAttention(GemmaAttention):
     SDPA API.
     """
 
-    # Ignore copy
+    # Adapted from GemmaAttention.forward
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -596,6 +483,7 @@ class GemmaSdpaAttention(GemmaAttention):
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
+        **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.
@@ -619,29 +507,17 @@ class GemmaSdpaAttention(GemmaAttention):
         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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, None
-        )
+        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)
@@ -657,15 +533,17 @@ class GemmaSdpaAttention(GemmaAttention):
             key_states = key_states.contiguous()
             value_states = value_states.contiguous()
 
-        # 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.
+        # 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
+
         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=causal_mask is None and q_len > 1,
+            is_causal=is_causal,
         )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
@@ -683,35 +561,28 @@ GEMMA_ATTENTION_CLASSES = {
 }
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LLAMA->GEMMA,Llama->Gemma
 class GemmaDecoderLayer(nn.Module):
     def __init__(self, config: GemmaConfig, layer_idx: int):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = GEMMA_ATTENTION_CLASSES[config._attn_implementation](
-            config=config, layer_idx=layer_idx
-        )
+        self.self_attn = GEMMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
 
         self.mlp = GemmaMLP(config)
         self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = GemmaRMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
+        self.post_attention_layernorm = GemmaRMSNorm(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[Tuple[torch.Tensor]] = None,
+        past_key_value: Optional[Cache] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
-    ) -> Tuple[
-        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
-    ]:
+    ) -> 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)`
@@ -725,12 +596,12 @@ class GemmaDecoderLayer(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
+            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)
@@ -790,12 +661,13 @@ class GemmaPreTrainedModel(PreTrainedModel):
     config_class = GemmaConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _keep_in_fp32_modules = ["inv_freq", "rotary_emb", "cos_cached", "sin_cached"]
     _no_split_modules = ["GemmaDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values", "causal_mask"]
+    _skip_keys_device_placement = ["past_key_values"]
     _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
@@ -808,31 +680,8 @@ class GemmaPreTrainedModel(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:
-            weights = layer.self_attn.o_proj.weight
-            layer.self_attn.past_key_value = cache_cls(
-                self.config,
-                max_batch_size,
-                max_cache_len,
-                device=weights.device,
-                dtype=weights.dtype,
-            )
 
-    def _reset_cache(self):
-        for layer in self.model.layers:
-            layer.self_attn.past_key_value = None
+_CONFIG_FOR_DOC = "GemmaConfig"
 
 
 GEMMA_INPUTS_DOCSTRING = r"""
@@ -913,7 +762,6 @@ GEMMA_INPUTS_DOCSTRING = r"""
     "The bare Gemma Model outputting raw hidden-states without any specific head on top.",
     GEMMA_START_DOCSTRING,
 )
-# Copied from transformers.models.llama.modeling_llama.LlamaModel with LLAMA->GEMMA,Llama->Gemma
 class GemmaModel(GemmaPreTrainedModel):
     """
     Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`]
@@ -927,14 +775,9 @@ class GemmaModel(GemmaPreTrainedModel):
         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(
-            [
-                GemmaDecoderLayer(config, layer_idx)
-                for layer_idx in range(config.num_hidden_layers)
-            ]
+            [GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
         self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.gradient_checkpointing = False
@@ -949,13 +792,12 @@ class GemmaModel(GemmaPreTrainedModel):
         self.embed_tokens = value
 
     @add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
-    # Ignore copy
     def forward(
         self,
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
@@ -963,20 +805,12 @@ class GemmaModel(GemmaPreTrainedModel):
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
     ) -> 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(
@@ -992,24 +826,24 @@ class GemmaModel(GemmaPreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        past_seen_tokens = 0
-        if use_cache:  # kept for BC (cache positions)
-            if not isinstance(past_key_values, StaticCache):
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_seen_tokens = past_key_values.get_seq_length()
+        return_legacy_cache = False  # noqa: F841
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True  # noqa: F841
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
             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_seen_tokens
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
         )
 
         # embed positions
@@ -1018,10 +852,17 @@ class GemmaModel(GemmaPreTrainedModel):
         # normalized
         # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
         # See https://github.com/huggingface/transformers/pull/29402
-        normalizer = torch.tensor(
-            self.config.hidden_size**0.5, dtype=hidden_states.dtype
-        )
+        normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
         hidden_states = hidden_states * normalizer
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
+            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 and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
+            )
 
         # decoder layers
         all_hidden_states = () if output_hidden_states else None
@@ -1068,19 +909,12 @@ class GemmaModel(GemmaPreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if isinstance(next_decoder_cache, Cache)
-                else next_decoder_cache
-            )
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_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,
@@ -1093,7 +927,8 @@ class GemmaModel(GemmaPreTrainedModel):
         attention_mask: torch.Tensor,
         input_tensor: torch.Tensor,
         cache_position: torch.Tensor,
-        past_seen_tokens: int,
+        past_key_values: Cache,
+        output_attentions: bool,
     ):
         # 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.
@@ -1105,90 +940,59 @@ class GemmaModel(GemmaPreTrainedModel):
                 return attention_mask
             return None
 
-        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.
+        # 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 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 hasattr(
-            getattr(self.layers[0], "self_attn", {}), "past_key_value"
-        ):  # static cache
-            target_length = self.config.max_position_embeddings
-        else:  # dynamic cache
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
             target_length = (
                 attention_mask.shape[-1]
                 if isinstance(attention_mask, torch.Tensor)
                 else past_seen_tokens + sequence_length + 1
             )
 
-        causal_mask = torch.full(
-            (sequence_length, target_length),
-            fill_value=min_dtype,
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
             dtype=dtype,
             device=device,
+            min_dtype=min_dtype,
+            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]
-                    + attention_mask[:, None, None, :]
-                )
-                padding_mask = padding_mask == 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
-            causal_mask = AttentionMaskConverter._unmask_unattended(
-                causal_mask, min_dtype
-            )
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
 
         return causal_mask
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->GEMMA,Llama->Gemma,llama->gemma
 class GemmaForCausalLM(GemmaPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
@@ -1219,17 +1023,14 @@ class GemmaForCausalLM(GemmaPreTrainedModel):
     def get_decoder(self):
         return self.model
 
-    # Ignore copy
     @add_start_docstrings_to_model_forward(GEMMA_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[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
@@ -1263,19 +1064,11 @@ class GemmaForCausalLM(GemmaPreTrainedModel):
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
         "What is your favorite condiment?"
         ```"""
-        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
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
+            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
 
         # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
         outputs = self.model(
@@ -1326,118 +1119,68 @@ class GemmaForCausalLM(GemmaPreTrainedModel):
         attention_mask=None,
         inputs_embeds=None,
         cache_position=None,
+        position_ids=None,
+        use_cache=True,
         **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 we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         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_length(), device=input_ids.device
-                    )
-                    if past_key_values.get_max_length() 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:]
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
 
-        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] :]
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
 
         # 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}
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
         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()}
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
 
-        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 isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
 
-        if has_static_cache:
-            past_key_values = None
+            dtype = self.lm_head.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
 
         model_inputs.update(
             {
                 "position_ids": position_ids,
                 "cache_position": cache_position,
                 "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
+                "use_cache": 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(
     """
@@ -1454,7 +1197,6 @@ class GemmaForCausalLM(GemmaPreTrainedModel):
     """,
     GEMMA_START_DOCSTRING,
 )
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->GEMMA,Llama->Gemma
 class GemmaForSequenceClassification(GemmaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
@@ -1477,7 +1219,7 @@ class GemmaForSequenceClassification(GemmaPreTrainedModel):
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
@@ -1491,9 +1233,7 @@ class GemmaForSequenceClassification(GemmaPreTrainedModel):
             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,
@@ -1515,25 +1255,19 @@ class GemmaForSequenceClassification(GemmaPreTrainedModel):
             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:
@@ -1541,9 +1275,7 @@ class GemmaForSequenceClassification(GemmaPreTrainedModel):
             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
-                ):
+                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"
@@ -1556,9 +1288,7 @@ class GemmaForSequenceClassification(GemmaPreTrainedModel):
                     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)
-                )
+                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)
@@ -1573,3 +1303,87 @@ class GemmaForSequenceClassification(GemmaPreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    The Gemma 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.
+    """,
+    GEMMA_START_DOCSTRING,
+)
+class GemmaForTokenClassification(GemmaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = GemmaModel(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(GEMMA_INPUTS_DOCSTRING)
+    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_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        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,
+        )