merge: upload transformers implementation

README.md

@@ -33,7 +33,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
   "stabilityai/stablelm-2-1_6b",
-  trust_remote_code=True,
   torch_dtype="auto",
 )
 model.cuda()
@@ -58,7 +57,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
   "stabilityai/stablelm-2-1_6b",
-  trust_remote_code=True,
   torch_dtype="auto",
   attn_implementation="flash_attention_2",
 )

config.json

@@ -1,11 +1,7 @@
 {
   "architectures": [
-    "StableLMEpochForCausalLM"
+    "StableLmForCausalLM"
   ],
-  "auto_map": {
-    "AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig",
-    "AutoModelForCausalLM": "modeling_stablelm_epoch.StableLMEpochForCausalLM"
-  },
   "bos_token_id": 100257,
   "eos_token_id": 100257,
   "hidden_act": "silu",
@@ -13,18 +9,16 @@
   "initializer_range": 0.02,
   "intermediate_size": 5632,
   "max_position_embeddings": 4096,
-  "model_type": "stablelm_epoch",
-  "norm_eps": 1e-05,
+  "model_type": "stablelm",
+  "layer_norm_eps": 1e-05,
   "num_attention_heads": 32,
-  "num_heads": 32,
   "num_hidden_layers": 24,
   "num_key_value_heads": 32,
-  "rope_pct": 0.25,
+  "partial_rotary_factor": 0.25,
   "rope_theta": 10000,
-  "rotary_scaling_factor": 1.0,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.36.2",
+  "transformers_version": "4.38.0",
   "use_cache": true,
   "use_qkv_bias": true,
   "vocab_size": 100352

configuration_stablelm_epoch.py → configuration_stablelm.py

@@ -1,4 +1,5 @@
-# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
+# coding=utf-8
+# Copyright 2024 Stability AI and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,32 +12,45 @@
 # 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.
-""" StableLM Epoch model configuration"""
-from transformers import PretrainedConfig
+""" StableLM model configuration """
+
+from transformers.configuration_utils import PretrainedConfig
 from transformers.utils import logging
 
 
 logger = logging.get_logger(__name__)
 
+STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "stabilityai/stablelm-3b-4e1t": "https://huggingface.co/stabilityai/stablelm-3b-4e1t/resolve/main/config.json",
+    # See all StableLM models at https://huggingface.co/models?filter=stablelm
+}
+
 
-class StableLMEpochConfig(PretrainedConfig):
+class StableLmConfig(PretrainedConfig):
     r"""
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
+    This is the configuration class to store the configuration of a [`~StableLmModel`].
+    It is used to instantiate an StableLM model according to the specified arguments, defining the model
+    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+    the StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.
+
+    Configuration objects inherit from  [`PretrainedConfig`] and can be used
+    to control the model outputs. Read the documentation from  [`PretrainedConfig`]
+    for more information.
+
 
     Args:
-        vocab_size (`int`, *optional*, defaults to 50_304):
+        vocab_size (`int`, *optional*, defaults to 50304):
             Vocabulary size of the StableLM model. Defines the number of different tokens that
-            can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
+            can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
         intermediate_size (`int`, *optional*, defaults to 6912):
             Dimension of the MLP representations.
         hidden_size (`int`, *optional*, defaults to 2560):
-            Dimension of the decoder layers and the pooler layer.
+            Number of hidden layers in the Transformer decoder.
         num_hidden_layers (`int`, *optional*, defaults to 32):
             Number of hidden layers in the Transformer decoder.
         num_attention_heads (`int`, *optional*, defaults to 32):
             Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*):
+        num_key_value_heads (`int`, *optional*, defaults to 32):
             This is the number of key_value heads that should be used to implement Grouped Query Attention. If
             `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
             `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
@@ -46,72 +60,124 @@ class StableLMEpochConfig(PretrainedConfig):
             `num_attention_heads`.
         hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
             The non-linear activation function (function or string).
-        rope_pct (`float`, *optional*, defaults to 1.0):
-            Percentage of hidden dimensions to allocate to rotary embeddings.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        max_position_embeddings (`int`, *optional*, defaults to 2048):
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        initializer_range (`float`, *optional*, defaults to 1e-5):
+        initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing
              all weight matrices.
-        norm_eps (`float`, *optional*, defaults to 1e-8):
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the normalization layers.
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether or not the model should return the last key/values attentions
             (not used by all models). Only relevant if `config.is_decoder=True`.
-        use_qkv_bias (`bool`, *optional*, defaults to `True`):
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to `10000.0`):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
+            is an experimental feature, subject to breaking API changes in future versions.
+        use_qkv_bias (`bool`, *optional*, defaults to `False`):
             Whether or not the model should use bias for qkv layers.
-        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
+        hidden_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after applying the MLP to the hidden states.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
-    """
-    model_type = "stablelm_epoch"
+        partial_rotary_factor (`float`, *optional*, defaults to 0.25):
+            Percentage of the query and keys which will have rotary embedding.
+        bos_token_id (int, *optional*, defaults to 0):
+            The id of the `BOS` token in the vocabulary.
+        eos_token_id (int, *optional*, defaults to 0):
+            The id of the `EOS` token in the vocabulary.
+
+    Example:
+
+    ```python
+    >>> from transformers import StableLmModel, StableLmConfig
+
+    >>> # Initializing a StableLM stablelm-3b style configuration
+    >>> configuration = StableLmConfig()
+    ```"""
+
+    model_type = "stablelm"
     keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,
-        vocab_size=50_304,
+        vocab_size=50304,
         intermediate_size=6912,
         hidden_size=2560,
         num_hidden_layers=32,
         num_attention_heads=32,
         num_key_value_heads=32,
         hidden_act="silu",
-        rope_pct=0.25,
-        rope_theta=10_000,
         max_position_embeddings=4096,
         initializer_range=0.02,
-        norm_eps=1.0e-5,
+        layer_norm_eps=1.0e-5,
         use_cache=True,
-        use_qkv_bias=True,
-        bos_token_id=0,
-        eos_token_id=2,
         tie_word_embeddings=False,
-        attention_dropout: float = 0.0,
+        rope_theta=10_000,
+        rope_scaling=None,
+        use_qkv_bias=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        partial_rotary_factor=0.25,
+        bos_token_id=0,
+        eos_token_id=0,
         **kwargs,
     ):
         self.vocab_size = vocab_size
         self.max_position_embeddings = max_position_embeddings
-        self.intermediate_size = intermediate_size
+
         self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
         self.num_hidden_layers = num_hidden_layers
         self.num_attention_heads = num_attention_heads
         self.num_key_value_heads = num_key_value_heads
         self.hidden_act = hidden_act
-        self.rope_pct = rope_pct
-        self.rope_theta = rope_theta
+
         self.initializer_range = initializer_range
-        self.norm_eps = norm_eps
+        self.layer_norm_eps = layer_norm_eps
         self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
         self.use_qkv_bias = use_qkv_bias
-        self.tie_word_embeddings = tie_word_embeddings
+        self.hidden_dropout = hidden_dropout
         self.attention_dropout = attention_dropout
+        self.partial_rotary_factor = partial_rotary_factor
+        self._rope_scaling_validation()
+
         super().__init__(
             bos_token_id=bos_token_id,
             eos_token_id=eos_token_id,
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

generation_config.json

@@ -2,5 +2,5 @@
   "_from_model_config": true,
   "bos_token_id": 100257,
   "eos_token_id": 100257,
-  "transformers_version": "4.36.2"
+  "transformers_version": "4.38.0"
 }

modeling_stablelm_epoch.py → modeling_stablelm.py

@@ -1,5 +1,10 @@
 # coding=utf-8
-# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,48 +17,48 @@
 # 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.
-#
-# This code is based off the following work:
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
-""" PyTorch StableLM Epoch model. """
-from typing import Optional, Tuple, Union
+""" PyTorch StableLM 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 CrossEntropyLoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
 from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging, is_flash_attn_greater_or_equal_2_10
+from transformers.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 .configuration_stablelm import StableLmConfig
 
-from .configuration_stablelm_epoch import StableLMEpochConfig
 
-try:
+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
-except:
-    flash_attn_func, flash_attn_varlen_func = None, None
-    index_first_axis, pad_input, unpad_input = None, None, None
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
 
 
 logger = logging.get_logger(__name__)
 
+_CONFIG_FOR_DOC = "StableLmConfig"
+
 
 # Copied from transformers.models.llama.modeling_llama._get_unpad_data
 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))
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
     return (
         indices,
         cu_seqlens,
@@ -61,113 +66,144 @@ def _get_unpad_data(attention_mask):
     )
 
 
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-):
-    """Make causal mask used for bi-directional self-attention."""
-    batch_size, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
-    batch_size, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(
-        inverted_mask.to(torch.bool), torch.finfo(dtype).min
-    )
-
-
-class RotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        max_position_embeddings: int,
-        base: int = 10_000,
-        device: Optional[torch.device] = None,
-    ):
+# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->StableLm
+class StableLmRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
 
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         # Build here to make `torch.jit.trace` work.
         self._set_cos_sin_cache(
-            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
         )
 
-    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
         self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
 
-        # Don't do einsum, it converts fp32 to fp16 under AMP
-        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
         freqs = torch.outer(t, self.inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
         emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
-    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
-        # x: [batch_size, num_heads, seq_len, head_size]
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
         if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
         return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
         )
 
 
-def rotate_half(x: torch.Tensor):
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->StableLm
+class StableLmLinearScalingRotaryEmbedding(StableLmRotaryEmbedding):
+    """StableLmRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->StableLm
+class StableLmDynamicNTKScalingRotaryEmbedding(StableLmRotaryEmbedding):
+    """StableLmRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        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(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
     """Rotates half the hidden dims of the input."""
-    x1, x2 = torch.chunk(x, 2, dim=-1)
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
     return torch.cat((-x2, x1), dim=-1)
 
 
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
     q_embed = (q * cos) + (rotate_half(q) * sin)
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
 
 
-class MLP(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
+# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->StableLm
+class StableLmMLP(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(config.hidden_size, config.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
-        self.act_fn = nn.SiLU()
+        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: torch.Tensor) -> torch.Tensor:
+    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,
@@ -180,48 +216,79 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
 
 
-class Attention(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
+class StableLmAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: StableLmConfig, layer_idx: Optional[int] = None):
         super().__init__()
         self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
         self.hidden_size = config.hidden_size
         self.num_heads = config.num_attention_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.partial_rotary_factor = config.partial_rotary_factor
         self.is_causal = True
-        self.attention_dropout = config.attention_dropout
 
         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.use_qkv_bias)
         self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_qkv_bias)
         self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_qkv_bias)
         self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
 
+        self.attention_dropout = nn.Dropout(config.attention_dropout)
         self._init_rope()
 
+    # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonAttention._init_rope with Persimmon->StableLm
     def _init_rope(self):
-        self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
-        self.rotary_emb = RotaryEmbedding(
-            self.rotary_ndims,
-            max_position_embeddings=self.config.max_position_embeddings,
-            base=self.config.rope_theta,
-        )
+        if self.config.rope_scaling is None:
+            self.rotary_emb = StableLmRotaryEmbedding(
+                int(self.partial_rotary_factor * self.head_dim),
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = StableLmLinearScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * 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 = StableLmDynamicNTKScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * 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,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        position_ids: torch.LongTensor,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
@@ -233,27 +300,37 @@ class Attention(nn.Module):
         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_rot = query_states[..., : self.rotary_ndims]
-        query_pass = query_states[..., self.rotary_ndims :]
-        key_rot = key_states[..., : self.rotary_ndims]
-        key_pass = key_states[..., self.rotary_ndims :]
-
         kv_seq_len = key_states.shape[-2]
         if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
         cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
 
-        # [batch_size, num_heads, seq_len, head_dim]
-        query_states = torch.cat((query_states, query_pass), dim=-1)
-        key_states = torch.cat((key_states, key_pass), dim=-1)
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
 
         if past_key_value is not None:
-            # Reuse k, v, self_attention
-            key_states = torch.cat((past_key_value[0], key_states), dim=2)
-            value_states = torch.cat((past_key_value[1], value_states), dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else None
+            # Specific to RoPE models with partial rotation
+            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
         # Repeat k/v heads if n_kv_heads < n_heads
         key_states = repeat_kv(key_states, self.num_key_value_groups)
@@ -274,9 +351,10 @@ class Attention(nn.Module):
                 )
             attn_weights = attn_weights + attention_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)
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query_states.dtype)
+        attn_weights = self.attention_dropout(attn_weights)
+
         attn_output = torch.matmul(attn_weights, value_states)
 
         if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
@@ -285,11 +363,9 @@ class Attention(nn.Module):
                 f" {attn_output.size()}"
             )
 
-        # Merge heads
         attn_output = attn_output.transpose(1, 2).contiguous()
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
-        # Final linear projection
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -298,11 +374,110 @@ class Attention(nn.Module):
         return attn_output, attn_weights, past_key_value
 
 
-class FlashAttention2(Attention):
+class StableLmSdpaAttention(StableLmAttention):
+    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,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> 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.
+            logger.warning_once(
+                "StableLmModel is using StableLmSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            # Specific to RoPE models with partial rotation
+            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout.p if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+class StableLmFlashAttention2(StableLmAttention):
     """
-    Reference: https://github.com/huggingface/transformers/blob/5d36025ca13d05151b7a0c761e90d429c4644a30/src/transformers/models/llama/modeling_llama.py#L456
+    StableLM flash attention module. This module inherits from `StableLmAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
     """
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -321,14 +496,7 @@ class FlashAttention2(Attention):
         use_cache: bool = False,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # FlashAttention2 attention does not support output_attentions
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
+        # StableLmFlashAttention2 attention does not support output_attentions
 
         output_attentions = False
 
@@ -345,27 +513,35 @@ class FlashAttention2(Attention):
         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_rot = query_states[..., : self.rotary_ndims]
-        query_pass = query_states[..., self.rotary_ndims :]
-        key_rot = key_states[..., : self.rotary_ndims]
-        key_pass = key_states[..., self.rotary_ndims :]
-
         kv_seq_len = key_states.shape[-2]
         if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
         cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
 
-        # [batch_size, num_heads, seq_len, head_dim]
-        query_states = torch.cat((query_states, query_pass), dim=-1)
-        key_states = torch.cat((key_states, key_pass), dim=-1)
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
 
         if past_key_value is not None:
-            # Reuse k, v, self_attention
-            key_states = torch.cat((past_key_value[0], key_states), dim=2)
-            value_states = torch.cat((past_key_value[1], value_states), dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else None
+            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
+            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.
@@ -376,8 +552,14 @@ class FlashAttention2(Attention):
         dropout_rate = self.attention_dropout if self.training else 0.0
 
         attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
         )
+
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
         attn_output = self.o_proj(attn_output)
 
@@ -386,6 +568,7 @@ class FlashAttention2(Attention):
 
         return attn_output, attn_weights, past_key_value
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
     def _flash_attention_forward(
         self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
     ):
@@ -411,7 +594,7 @@ class FlashAttention2(Attention):
         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 FlashAttention2 __init__.
+            # 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
@@ -445,6 +628,7 @@ class FlashAttention2(Attention):
 
         return attn_output
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
     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
@@ -485,28 +669,51 @@ class FlashAttention2(Attention):
 
 
 ATTENTION_CLASSES = {
-    "eager": Attention,
-    "flash_attention_2": FlashAttention2,
+    "eager": StableLmAttention,
+    "sdpa": StableLmSdpaAttention,
+    "flash_attention_2": StableLmFlashAttention2,
 }
 
 
-class DecoderLayer(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
+class StableLmDecoderLayer(nn.Module):
+    def __init__(self, config: StableLmConfig, layer_idx: int):
         super().__init__()
-        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](config=config)
-        self.mlp = MLP(config)
-        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.hidden_size = config.hidden_size
+        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
+        self.mlp = StableLmMLP(config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout)
 
     def forward(
         self,
-        hidden_states: Optional[torch.FloatTensor],
-        attention_mask: Optional[torch.FloatTensor] = None,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
-    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[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)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
+                `[0, config.n_positions - 1]`.
+
+                [What are position IDs?](../glossary#position-ids)
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*):
+                cached past key and value projection states
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+        """
+
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -526,7 +733,9 @@ class DecoderLayer(nn.Module):
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = hidden_states + residual
 
         outputs = (hidden_states,)
 
@@ -539,45 +748,143 @@ class DecoderLayer(nn.Module):
         return outputs
 
 
-class StableLMEpochPreTrainedModel(PreTrainedModel):
-    """An abstract class to handle weights initialization and a simple interface
-    for downloading and loading pretrained models.
-    """
+STABLELM_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`StableLmConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
 
-    config_class = StableLMEpochConfig
+
+@add_start_docstrings(
+    "The bare StableLm Model outputting raw hidden-states without any specific head on top.",
+    STABLELM_START_DOCSTRING,
+)
+class StableLmPreTrainedModel(PreTrainedModel):
+    config_class = StableLmConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["DecoderLayer"]
+    _no_split_modules = ["StableLmDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn_2 = True
+    _supports_cache_class = True
+    _supports_sdpa = True
 
-    def _init_weights(self, module: nn.Module):
-        """Initialize the weights"""
+    def _init_weights(self, module):
+        std = self.config.initializer_range
         if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            module.weight.data.normal_(mean=0.0, std=std)
             if module.bias is not None:
                 module.bias.data.zero_()
         elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            module.weight.data.normal_(mean=0.0, std=std)
             if module.padding_idx is not None:
                 module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
 
-    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
-        if isinstance(module, StableLMEpochModel):
-            module.gradient_checkpointing = value
 
+STABLELM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            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;
+            - 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.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare StableLm Model outputting raw hidden-states without any specific head on top.",
+    STABLELM_START_DOCSTRING,
+)
+class StableLmModel(StableLmPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`StableLmDecoderLayer`]
+
+    Args:
+        config: StableLmConfig
+    """
 
-class StableLMEpochModel(StableLMEpochPreTrainedModel):
-    def __init__(self, config: StableLMEpochConfig):
+    def __init__(self, config: StableLmConfig):
         super().__init__(config)
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
-        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+        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.layers = nn.ModuleList(
+            [StableLmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self._attn_implementation = config._attn_implementation
         self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
@@ -585,43 +892,16 @@ class StableLMEpochModel(StableLMEpochPreTrainedModel):
     def get_input_embeddings(self):
         return self.embed_tokens
 
-    def set_input_embeddings(self, value: nn.Module):
+    def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_shape: torch.Size,
-        inputs_embeds: torch.Tensor,
-        past_key_values_length: int,
-    ):
-        # Create causal mask
-        # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(
-                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
-            ).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-
-        return combined_attention_mask
-
+    @add_start_docstrings_to_model_forward(STABLELM_INPUTS_DOCSTRING)
     def forward(
         self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[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,
@@ -629,103 +909,90 @@ class StableLMEpochModel(StableLMEpochPreTrainedModel):
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, BaseModelOutputWithPast]:
         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 = (
+            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
 
-        # Retrieve input_ids and inputs_embeds
+        # retrieve input_ids and inputs_embeds
         if input_ids is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
-            )
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
         elif input_ids is not None:
             batch_size, seq_length = input_ids.shape
         elif inputs_embeds is not None:
             batch_size, seq_length, _ = inputs_embeds.shape
         else:
-            raise ValueError(
-                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
-            )
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
 
         seq_length_with_past = seq_length
         past_key_values_length = 0
 
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
         if position_ids is None:
             device = input_ids.device if input_ids is not None else inputs_embeds.device
             position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
             )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
+            position_ids = position_ids.unsqueeze(0)
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
-        # Embed positions
-        if self._use_flash_attention_2:
+        # embed positions
+        if self._attn_implementation == "flash_attention_2":
             # 2d mask is passed through the layers
             attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        # for output_attentions case used fallback to eager attention realization
+        elif self._attn_implementation == "sdpa" and not output_attentions:
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
         else:
-            if attention_mask is None:
-                attention_mask = torch.ones(
-                    (batch_size, seq_length_with_past),
-                    dtype=torch.bool,
-                    device=inputs_embeds.device,
-                )
-            attention_mask = self._prepare_decoder_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
             )
 
         hidden_states = inputs_embeds
 
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        # Decoder layers
+        # decoder layers
         all_hidden_states = () if output_hidden_states else None
         all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
+        next_decoder_cache = None
 
-        for idx, decoder_layer in enumerate(self.layers):
+        for decoder_layer in self.layers:
             if output_hidden_states:
                 all_hidden_states += (hidden_states,)
 
-            past_key_value = (
-                past_key_values[idx] if past_key_values is not None else None
-            )
-
             if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
                     hidden_states,
                     attention_mask,
                     position_ids,
+                    past_key_values,
+                    output_attentions,
                 )
             else:
                 layer_outputs = decoder_layer(
                     hidden_states,
                     attention_mask=attention_mask,
                     position_ids=position_ids,
-                    past_key_value=past_key_value,
+                    past_key_value=past_key_values,
                     output_attentions=output_attentions,
                     use_cache=use_cache,
                 )
@@ -733,24 +1000,23 @@ class StableLMEpochModel(StableLMEpochPreTrainedModel):
             hidden_states = layer_outputs[0]
 
             if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
 
             if output_attentions:
                 all_self_attns += (layer_outputs[1],)
 
         hidden_states = self.norm(hidden_states)
 
-        # Add hidden states from the last decoder layer
+        # add hidden states from the last decoder layer
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = next_decoder_cache if use_cache else None
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else 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,
@@ -759,42 +1025,53 @@ class StableLMEpochModel(StableLMEpochPreTrainedModel):
         )
 
 
-class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
+# Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM with PERSIMMON->STABLELM,Persimmon->StableLm
+class StableLmForCausalLM(StableLmPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
-    def __init__(self, config: StableLMEpochConfig):
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->STABLELM,Llama->StableLm
+    def __init__(self, config):
         super().__init__(config)
-
-        self.model = StableLMEpochModel(config)
+        self.model = StableLmModel(config)
+        self.vocab_size = config.vocab_size
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
         # Initialize weights and apply final processing
         self.post_init()
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
     def get_input_embeddings(self):
         return self.model.embed_tokens
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
     def set_input_embeddings(self, value):
         self.model.embed_tokens = value
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
     def get_output_embeddings(self):
         return self.lm_head
 
-    def set_output_embeddings(self, new_embeddings: nn.Module):
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
         self.lm_head = new_embeddings
 
-    def get_decoder(self):
-        return self.model
-
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
     def set_decoder(self, decoder):
         self.model = decoder
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(STABLELM_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    # Ignore copy
     def forward(
         self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[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,
@@ -802,23 +1079,40 @@ class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                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]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, StableLmForCausalLM
+
+        >>> model = StableLmForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t")
+        >>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
+
+        >>> prompt = "The weather is always wonderful in"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        'The weather is always wonderful in the summer in the city of San Diego. The city is located on the coast of the Pacific Ocean and is surrounded by'
+        ```"""
+
+        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(
-            input_ids,
+            input_ids=input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,
@@ -830,7 +1124,7 @@ class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states).float()
+        logits = self.lm_head(hidden_states)
 
         loss = None
         if labels is not None:
@@ -858,35 +1152,46 @@ class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
         )
 
     def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
     ):
-        # Trim decoder_input_ids if past is used
         if past_key_values is not None:
-            past_length = past_key_values[0][0].shape[2]
-
-            # Some generation methods already pass only the last input ID
-            if input_ids.shape[1] > past_length:
-                remove_prefix_length = past_length
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
             else:
-                # Default to old behavior: keep only final ID
-                remove_prefix_length = input_ids.shape[1] - 1
-
-            input_ids = input_ids[:, remove_prefix_length:]
+                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
+            # 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[:, -1].unsqueeze(-1)
+                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` 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:
@@ -894,10 +1199,10 @@ class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
 
         model_inputs.update(
             {
-                "attention_mask": attention_mask,
+                "position_ids": position_ids,
                 "past_key_values": past_key_values,
                 "use_cache": kwargs.get("use_cache"),
-                "position_ids": position_ids,
+                "attention_mask": attention_mask,
             }
         )
         return model_inputs
@@ -907,13 +1212,130 @@ class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
         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
-                ),
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
             )
         return reordered_past
 
 
-StableLMEpochConfig.register_for_auto_class()
-StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")
+@add_start_docstrings(
+    """
+    The StableLm transformer with a sequence classification head on top (linear layer).
+
+    [`StableLmForSequenceClassification`] uses the last token in order to do the classification, as other causal
+    models (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    STABLELM_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->STABLELM,Llama->StableLm
+class StableLmForSequenceClassification(StableLmPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = StableLmModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # 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(STABLELM_INPUTS_DOCSTRING)
+    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,
+        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, SequenceClassifierOutputWithPast]:
+        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
+
+        transformer_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,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            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.")
+        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 = 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]
+
+        loss = None
+        if labels is not None:
+            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
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )