Rename modeling_stablelm.py to modeling_stablelm_epoch.py

modeling_stablelm.py → modeling_stablelm_epoch.py

@@ -1,10 +1,5 @@
 # coding=utf-8
-# 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.
+# Copyright 2023 Stability AI, EleutherAI, 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.
@@ -17,48 +12,48 @@
 # 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.
-""" PyTorch StableLM model."""
+#
+# 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
 import math
-from typing import List, Optional, Tuple, Union
+import warnings
 
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.nn import CrossEntropyLoss
 
-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 (
-    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.cache_utils import Cache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
 )
-from .configuration_stablelm import StableLmConfig
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging, is_flash_attn_greater_or_equal_2_10
 
+from .configuration_stablelm_epoch import StableLMEpochConfig
 
-if is_flash_attn_2_available():
+try:
     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
+    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
 
 
 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.int32), (1, 0))
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
     return (
         indices,
         cu_seqlens,
@@ -66,144 +61,113 @@ def _get_unpad_data(attention_mask):
     )
 
 
-# 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):
+# 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,
+    ):
         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, dtype=torch.int64).float().to(device) / self.dim))
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / 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, device, dtype):
+    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.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 = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
 
+        # 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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+        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)
 
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
+    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
+        # x: [batch_size, num_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=x.dtype)
-
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_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),
         )
 
 
-# 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):
+def rotate_half(x: torch.Tensor):
     """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
+    x1, x2 = torch.chunk(x, 2, dim=-1)
     return torch.cat((-x2, x1), dim=-1)
 
 
-# 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)
+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]
     q_embed = (q * cos) + (rotate_half(q) * sin)
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
 
 
-# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->StableLm
-class StableLmMLP(nn.Module):
-    def __init__(self, config):
+class MLP(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
         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)
-        self.act_fn = ACT2FN[config.hidden_act]
+        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()
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         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,
@@ -216,28 +180,16 @@ 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 StableLmAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: StableLmConfig, layer_idx: Optional[int] = None):
+class Attention(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
         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
 
         if (self.head_dim * self.num_heads) != self.hidden_size:
@@ -245,50 +197,30 @@ class StableLmAttention(nn.Module):
                 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):
-        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}")
+        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,
+        )
 
     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,
+        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,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
@@ -300,37 +232,27 @@ class StableLmAttention(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:
-            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)
+            kv_seq_len += past_key_value[0].shape[-2]
         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)
 
-        # 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)
+        # [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)
 
         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)
+            # 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
 
         # Repeat k/v heads if n_kv_heads < n_heads
         key_states = repeat_kv(key_states, self.num_key_value_groups)
@@ -351,10 +273,8 @@ class StableLmAttention(nn.Module):
                 )
             attn_weights = attn_weights + attention_mask
 
-        # 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)
-
+        # Upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
         attn_output = torch.matmul(attn_weights, value_states)
 
         if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
@@ -363,9 +283,11 @@ class StableLmAttention(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:
@@ -374,110 +296,11 @@ class StableLmAttention(nn.Module):
         return attn_output, attn_weights, past_key_value
 
 
-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):
+class FlashAttention2(Attention):
     """
-    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.
+    Reference: https://github.com/huggingface/transformers/blob/5d36025ca13d05151b7a0c761e90d429c4644a30/src/transformers/models/llama/modeling_llama.py#L456
     """
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -496,7 +319,14 @@ class StableLmFlashAttention2(StableLmAttention):
         use_cache: bool = False,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # StableLmFlashAttention2 attention does not support output_attentions
+        # 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")
 
         output_attentions = False
 
@@ -513,35 +343,27 @@ class StableLmFlashAttention2(StableLmAttention):
         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:
-            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)
+            kv_seq_len += past_key_value[0].shape[-2]
         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)
 
-        # 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)
+        # [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)
 
         if past_key_value is not 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)
+            # 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
 
         # 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.
@@ -552,14 +374,8 @@ class StableLmFlashAttention2(StableLmAttention):
         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)
 
@@ -568,7 +384,6 @@ class StableLmFlashAttention2(StableLmAttention):
 
         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
     ):
@@ -594,7 +409,7 @@ class StableLmFlashAttention2(StableLmAttention):
         if not self._flash_attn_uses_top_left_mask:
             causal = self.is_causal
         else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            # 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__.
             causal = self.is_causal and query_length != 1
 
         # Contains at least one padding token in the sequence
@@ -628,7 +443,6 @@ class StableLmFlashAttention2(StableLmAttention):
 
         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
@@ -669,51 +483,28 @@ class StableLmFlashAttention2(StableLmAttention):
 
 
 ATTENTION_CLASSES = {
-    "eager": StableLmAttention,
-    "sdpa": StableLmSdpaAttention,
-    "flash_attention_2": StableLmFlashAttention2,
+    "eager": Attention,
+    "flash_attention_2": FlashAttention2,
 }
 
 
-class StableLmDecoderLayer(nn.Module):
-    def __init__(self, config: StableLmConfig, layer_idx: int):
+class DecoderLayer(nn.Module):
+    def __init__(self, config: StableLMEpochConfig):
         super().__init__()
-        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)
+        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)
 
     def forward(
         self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
+        hidden_states: Optional[torch.FloatTensor],
+        attention_mask: Optional[torch.FloatTensor] = 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,
-    ) -> 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`).
-        """
-
+    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -733,9 +524,7 @@ class StableLmDecoderLayer(nn.Module):
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
-
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = hidden_states + residual
+        hidden_states = residual + hidden_states
 
         outputs = (hidden_states,)
 
@@ -748,143 +537,45 @@ class StableLmDecoderLayer(nn.Module):
         return outputs
 
 
-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.
-"""
-
+class StableLMEpochPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and a simple interface
+    for downloading and loading pretrained models.
+    """
 
-@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"
+    config_class = StableLMEpochConfig
+    base_model_prefix = "transformer"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["StableLmDecoderLayer"]
+    _no_split_modules = ["DecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn_2 = True
-    _supports_cache_class = True
-    _supports_sdpa = True
 
-    def _init_weights(self, module):
-        std = self.config.initializer_range
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if module.bias is not None:
                 module.bias.data.zero_()
         elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             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
-    """
 
-    def __init__(self, config: StableLmConfig):
+class StableLMEpochModel(StableLMEpochPreTrainedModel):
+    def __init__(self, config: StableLMEpochConfig):
         super().__init__(config)
-        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.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._attn_implementation = config._attn_implementation
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
         self.gradient_checkpointing = False
         # Initialize weights and apply final processing
         self.post_init()
@@ -892,16 +583,43 @@ class StableLmModel(StableLmPreTrainedModel):
     def get_input_embeddings(self):
         return self.embed_tokens
 
-    def set_input_embeddings(self, value):
+    def set_input_embeddings(self, value: nn.Module):
         self.embed_tokens = value
 
-    @add_start_docstrings_to_model_forward(STABLELM_INPUTS_DOCSTRING)
+    # 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
+
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
@@ -909,90 +627,103 @@ class StableLmModel(StableLmPreTrainedModel):
         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)
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
-        # embed positions
-        if self._attn_implementation == "flash_attention_2":
+        # Embed positions
+        if self._use_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:
-            # 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
+            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,
             )
 
         hidden_states = inputs_embeds
 
-        # decoder layers
+        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
         all_hidden_states = () if output_hidden_states else None
         all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
+        next_decoder_cache = () if use_cache else None
 
-        for decoder_layer in self.layers:
+        for idx, decoder_layer in enumerate(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:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
+
+                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),
                     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_values,
+                    past_key_value=past_key_value,
                     output_attentions=output_attentions,
                     use_cache=use_cache,
                 )
@@ -1000,23 +731,24 @@ class StableLmModel(StableLmPreTrainedModel):
             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 = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-
+        next_cache = next_decoder_cache if use_cache else None
         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,
@@ -1025,53 +757,42 @@ class StableLmModel(StableLmPreTrainedModel):
         )
 
 
-# Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM with PERSIMMON->STABLELM,Persimmon->StableLm
-class StableLmForCausalLM(StableLmPreTrainedModel):
+class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->STABLELM,Llama->StableLm
-    def __init__(self, config):
+    def __init__(self, config: StableLMEpochConfig):
         super().__init__(config)
-        self.model = StableLmModel(config)
-        self.vocab_size = config.vocab_size
+
+        self.model = StableLMEpochModel(config)
         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
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
+    def set_output_embeddings(self, new_embeddings: nn.Module):
         self.lm_head = new_embeddings
 
-    # 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 set_decoder(self, decoder):
+        self.model = decoder
+
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
@@ -1079,40 +800,23 @@ class StableLmForCausalLM(StableLmPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
-        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_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
         output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
         )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
         outputs = self.model(
-            input_ids=input_ids,
+            input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,
@@ -1124,7 +828,7 @@ class StableLmForCausalLM(StableLmPreTrainedModel):
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+        logits = self.lm_head(hidden_states).float()
 
         loss = None
         if labels is not None:
@@ -1152,46 +856,35 @@ class StableLmForCausalLM(StableLmPreTrainedModel):
         )
 
     def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+        self,
+        input_ids,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
     ):
+        # Trim decoder_input_ids if past is used
         if past_key_values is not None:
-            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()
+            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
             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:]
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_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[:, -input_ids.shape[1] :]
+                position_ids = position_ids[:, -1].unsqueeze(-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:
@@ -1199,10 +892,10 @@ class StableLmForCausalLM(StableLmPreTrainedModel):
 
         model_inputs.update(
             {
-                "position_ids": position_ids,
+                "attention_mask": attention_mask,
                 "past_key_values": past_key_values,
                 "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
+                "position_ids": position_ids,
             }
         )
         return model_inputs
@@ -1212,130 +905,13 @@ class StableLmForCausalLM(StableLmPreTrainedModel):
         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
 
 
-@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,
-        )
+StableLMEpochConfig.register_for_auto_class()
+StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")