init release

config.json

@@ -0,0 +1,41 @@
+{
+  "architectures": [
+    "LongLlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "gradient_checkpoint_every_ith": 1,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "last_context_length": 1024,
+  "max_position_embeddings": 4096,
+  "mem_attention_grouping": null,
+  "mem_dtype": "bfloat16",
+  "mem_layers": [
+    8,
+    16,
+    24
+  ],
+  "mem_positionals": true,
+  "model_type": "longllama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "tie_word_embeddings": false,
+  "torch_attention": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.30.0",
+  "use_cache": true,
+  "vocab_size": 32016,
+  "auto_map": {
+    "AutoConfig": "configuration_longllama.LongLlamaConfig",
+    "AutoModel": "modeling_longllama.LongLlamaModel",
+    "AutoModelForCausalLM": "modeling_longllama.LongLlamaForCausalLM",
+    "AutoModelForSequenceClassification": "modeling_longllama.LongLlamaForSequenceClassification"
+  }
+}

configuration_longllama.py

@@ -0,0 +1,163 @@
+# coding=utf-8
+# Copyright 2023 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+""" LongLLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+LONGLLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "syzymon/long_llama_3b": "https://huggingface.co/syzymon/long_llama_3b/resolve/main/config.json",
+}
+
+
+class LongLlamaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LongLlamaModel`]. It is used to instantiate an LongLLaMA
+    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 LongLLaMA-7B.
+
+    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 32000):
+            Vocabulary size of the LongLLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LongLlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            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 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms 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`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        mem_layers (`List[int]`, defaults to `[]`):
+            Layers with memory
+        mem_positionals (`bool`, *optional*, defaults to `True`):
+            Whether to use positional embeddings in memory layers
+        mem_dtype (`str`, *optional*, defaults to `"bfloat16"`):
+            Type for keys and values stored in memory
+        mem_attention_grouping (`Tuple[int, int]`, *optional*, defaults to `None`):
+            One can trade speed for memory by performing attention
+            in memory layers sequentially.
+            When equal to `(4, 2048)` the memory layers will process at most 4 heads and 2048 queries from each head at once.
+            That is at most 4*2048 queries at once.
+        torch_attention (`bool`, *optional*, defaults to `False`):
+            Whether to use torch scaled_dot_product_attention
+        gradient_checkpoint_every_ith (`int`, *optional*, defaults to `1`):
+            When gradient checkpointing is enabled checkpoint every ith layer
+
+        Example:
+
+    ```python
+    >>> from transformers import LongLlamaModel, LongLlamaConfig
+
+    >>> # Initializing a LongLLaMA longllama-7b style configuration
+    >>> configuration = LongLlamaConfig()
+
+    >>> # Initializing a model from the longllama-7b style configuration
+    >>> model = LongLlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "longllama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        last_context_length=1024,
+        mem_layers=[],
+        mem_positionals=True,
+        mem_dtype="bfloat16",
+        mem_attention_grouping=None,
+        torch_attention=False,
+        gradient_checkpoint_every_ith=1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        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.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.last_context_length = last_context_length
+        self.mem_layers = mem_layers
+        self.mem_positionals = mem_positionals
+        self.mem_dtype = mem_dtype
+        self.mem_attention_grouping = mem_attention_grouping
+        self.torch_attention = torch_attention
+        self.gradient_checkpoint_every_ith = gradient_checkpoint_every_ith
+
+        self._rope_scaling_validation()
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is not None:
+            raise ValueError("LongLLaMA does not use rope_scaling")

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.33.0.dev0"
+}

longllama_utils.py

@@ -0,0 +1,64 @@
+from collections import namedtuple
+from dataclasses import dataclass
+import torch
+from typing import Tuple, Optional
+
+
+@dataclass
+class LongLlamaMemConfig:
+    """
+    Class for configuring memory caches for LongLlama model.
+
+    Args:
+        positionals (`boolean`)
+            Whether to use positional embeddings in memory layer
+        cache_dtype (`torch.dtype`)
+            Specifies storing type for keys and values
+        attention_grouping (`Tuple[int, int]`, *optional*)
+            One can trade speed for memory by performing attention
+            in memory layers sequentially.
+            When equal to `(4, 128)` the memory layers will process at most 4 heads and 128 queries
+            from each head at once. That is at most 512 queries at once.
+    """
+
+    positionals: bool = True
+    cache_dtype: torch.dtype = torch.bfloat16
+    attention_grouping: Optional[Tuple[int, int]] = None
+
+
+@dataclass
+class LongLlamaMemCache:
+    """
+    Class with LongLlama's memory cache
+
+    Args:
+        keys (`torch.FloatTensor` of shape `(batch_size, num_heads, mem_length, embed_size_per_head)`)
+        values (`torch.FloatTensor` of shape `(batch_size, num_heads, mem_length, embed_size_per_head)`)
+        masks (`torch.FloatTensor` of shape `(batch_size, 1, mem_length, 1)`)
+            For masking out parts of memory
+    """
+
+    keys: torch.FloatTensor
+    values: torch.FloatTensor
+    masks: torch.FloatTensor
+
+
+def mem_apply_update(
+    prev_mem_cache: LongLlamaMemCache, new_mem_content: LongLlamaMemCache, mem_config: LongLlamaMemConfig
+):
+    def update_one(prev, new):
+        if len(prev.shape) != 4 or len(new.shape) != 4:
+            raise ValueError(f"Memory cache content should be consistent in shape got {prev.shape} {new.shape}")
+
+        return torch.concat([prev, new], dim=-2)
+
+    insert_size = new_mem_content.keys.shape[-2]
+
+    if new_mem_content.values.shape[-2] != insert_size or new_mem_content.masks.shape[-2] != insert_size:
+        raise ValueError(f"Inconsistent mem_length in new_mem_content")
+
+    return LongLlamaMemCache(
+        keys=update_one(prev_mem_cache.keys, new_mem_content.keys),
+        values=update_one(prev_mem_cache.values, new_mem_content.values),
+        masks=update_one(prev_mem_cache.masks, new_mem_content.masks),
+    )

modeling_longllama.py

@@ -0,0 +1,1467 @@
+# coding=utf-8
+# Copyright 2023 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 LongLLaMA model."""
+from dataclasses import dataclass
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+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,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_longllama import LongLlamaConfig
+from .longllama_utils import mem_apply_update, LongLlamaMemCache, LongLlamaMemConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LongLlamaConfig"
+
+
+@dataclass
+class LongLlamaModelOutputWithPast(BaseModelOutputWithPast):
+    """
+    Based on BaseModelOutputWithPast
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+        mem_caches  (`tuple(LongLlamaMemCache))`, *optional*, returned for layers with memory cache enabled):
+            For the layers without memory None is returned
+    """
+
+    mem_caches: Optional[LongLlamaMemCache] = 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.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->LongLlama
+class LongLlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LongLlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->LongLlama
+class LongLlamaRotaryEmbedding(torch.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).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()
+        )
+
+    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=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", 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)
+
+    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=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Based on transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def rotate_one(x, cos, sin, position_ids):
+    if len(position_ids.shape) != 2 or x.shape[0] != position_ids.shape[0] or x.shape[-2] != position_ids.shape[1]:
+        raise ValueError(f"Position ids shoud have shape [bsz, seq_len] got {position_ids.shape}")
+    # 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)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    x_embed = (x * cos) + (rotate_half(x) * sin)
+    return x_embed
+
+
+def rotate_as_if_first(x, rotary_emb):
+    # x: [bs, num_attention_heads, seq_len, head_size]
+    # apply rotary as if all elements were first in the sequence
+    cos, sin = rotary_emb(x, x.shape[-2])
+    return rotate_one(x, cos, sin, torch.zeros(x.shape[0], x.shape[-2], dtype=torch.long, device=cos.device))
+
+
+# Based on an 4.30 transformers.models.llama.modeling_llama.LlamaMLP with Llama->LongLlama
+class LongLlamaMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# Modified transformers.models.llama.modeling_llama.LlamaAttention
+class LongLlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper with FoT modifications"""
+
+    def __init__(self, config: LongLlamaConfig, mem_config: Optional[LongLlamaMemConfig] = None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.max_cache = self.max_position_embeddings
+        self.rope_theta = config.rope_theta
+
+        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=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self._init_rope()
+        self.mem_config = mem_config
+
+    def _init_rope(self):
+        assert self.config.rope_scaling is None
+        self.rotary_emb = LongLlamaRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        mem_cache: Optional[LongLlamaMemCache] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if attention_mask is None:
+            tgt_seq_len = hidden_states.shape[-2]
+            if past_key_value is not None:
+                src_seq_len = past_key_value[0].shape[-2] + tgt_seq_len
+            else:
+                src_seq_len = tgt_seq_len
+
+            attention_mask = torch.zeros(
+                hidden_states.shape[0],
+                1,
+                tgt_seq_len,
+                src_seq_len,
+                device=hidden_states.device,
+                dtype=hidden_states.dtype,
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        position_ids = position_ids[:, None, :, None]
+
+        if position_ids.shape != (key_states.shape[0], 1, key_states.shape[-2], 1):
+            raise ValueError("position_ids should match batch and seq_len of the input")
+
+        mem_no_local_cache = self.mem_config is not None and past_key_value is None and (not use_cache)
+        mem_and_local_cache = self.mem_config is not None and use_cache
+        # positonal embeddings can be disabled for memory layers
+        use_positionals = self.mem_config is None or self.mem_config.positionals
+
+        if mem_no_local_cache:
+            # the whole context window will be moved to memory cache after the attention
+            if use_positionals:
+                # positionally embedd memory content as first token in the sequence
+                rfst_key_states = rotate_as_if_first(key_states, self.rotary_emb)
+            else:
+                rfst_key_states = key_states
+            # attention_mask [bsz, 1, tgt_seq_len, src_seq_len]
+            # we base the mask on the last token in the context window
+            mem_update = LongLlamaMemCache(
+                keys=rfst_key_states.to(self.mem_config.cache_dtype),
+                values=value_states.to(self.mem_config.cache_dtype),
+                masks=attention_mask[..., -1, :, None],
+            )
+
+        if past_key_value is not None:
+            past_local_cache_size = past_key_value[0].shape[-2]
+            key_states = torch.cat([past_key_value[0], key_states], dim=-2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=-2)
+            # FoT additionally stores position_ids to support long inputs
+            position_ids = torch.cat([past_key_value[2], position_ids], dim=-2)
+
+            if attention_mask.shape[-1] != key_states.shape[-2] and attention_mask.shape[-2] != query_states.shape[-2]:
+                raise ValueError("attention_mask should be provided for all key_states in local context")
+
+            # local cache is maintained so that it is <= self.max_cache
+            # remaining elements are either dropped or go to memory cache
+            if key_states.shape[-2] > self.max_cache:
+                num_elems_to_drop = past_local_cache_size
+
+                if mem_and_local_cache:
+                    drop_keys = key_states[:, :, :num_elems_to_drop, :]
+                    drop_values = value_states[:, :, :num_elems_to_drop, :]
+                    # as memory mask use the masking of the last key in context
+                    # attention_mask [bsz, 1, tgt_seq_len, src_seq_len]
+                    drop_masks = attention_mask[..., -1, :, None]
+                    drop_masks = drop_masks[:, :, :num_elems_to_drop, :]
+
+                    if use_positionals:
+                        rfst_drop_keys = rotate_as_if_first(drop_keys, self.rotary_emb)
+                    else:
+                        rfst_drop_keys = drop_keys
+                    mem_update = LongLlamaMemCache(
+                        keys=rfst_drop_keys.to(self.mem_config.cache_dtype),
+                        values=drop_values.to(self.mem_config.cache_dtype),
+                        masks=drop_masks,
+                    )
+                    if mem_cache is None:
+                        mem_cache = mem_update
+                    else:
+                        mem_cache = mem_apply_update(
+                            prev_mem_cache=mem_cache, new_mem_content=mem_update, mem_config=self.mem_config
+                        )
+
+                key_states = key_states[:, :, num_elems_to_drop:, :]
+                value_states = value_states[:, :, num_elems_to_drop:, :]
+                position_ids = position_ids[:, :, num_elems_to_drop:, :]
+                attention_mask = attention_mask[..., num_elems_to_drop:]
+
+        # FoT additionally stores position_ids to support long inputs
+        past_key_value = (key_states, value_states, position_ids) if use_cache else None
+
+        kv_seq_len = key_states.shape[-2]
+
+        if use_positionals:
+            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+            rel_pos_ids = position_ids - torch.min(position_ids, dim=-2, keepdim=True)[0]
+            rel_pos_ids = rel_pos_ids.squeeze(3).squeeze(1)
+
+            query_states = rotate_one(query_states, cos, sin, rel_pos_ids[:, -query_states.shape[-2] :])
+            key_states = rotate_one(key_states, cos, sin, rel_pos_ids)
+
+        if self.mem_config is not None and self.mem_config.attention_grouping is not None:
+            attn_grouping_h, attn_grouping_q = self.mem_config.attention_grouping
+            if attn_grouping_h <= 0 or attn_grouping_q <= 0:
+                raise ValueError("Attention grouping should be positive")
+        else:
+            attn_grouping_h, attn_grouping_q = self.num_heads, q_len
+
+        attn_output_h = []
+        for beg_h in range(0, self.num_heads, attn_grouping_h):
+            end_h = min(beg_h + attn_grouping_h, self.num_heads)
+
+            attn_output_q = []
+            for beg_q in range(0, q_len, attn_grouping_q):
+                end_q = min(beg_q + attn_grouping_q, q_len)
+
+                if self.config.torch_attention:
+                    if mem_cache is not None:
+                        attn_keys = torch.concat(
+                            [key_states[:, beg_h:end_h], mem_cache.keys[:, beg_h:end_h].to(key_states.dtype)], dim=-2
+                        )
+                        attn_values = torch.concat(
+                            [value_states[:, beg_h:end_h], mem_cache.values[:, beg_h:end_h].to(value_states.dtype)],
+                            dim=-2,
+                        )
+                        mem_mask = mem_cache.masks.squeeze(-1).unsqueeze(-2)
+                        assert len(mem_mask.shape) == 4
+                        assert mem_mask.shape[2] == 1
+                        assert mem_mask.shape[3] == mem_cache.keys.shape[-2]
+                        mem_mask = torch.broadcast_to(
+                            mem_mask, (mem_mask.shape[0], mem_mask.shape[1], end_q - beg_q, mem_mask.shape[3])
+                        )
+                        attn_mask = torch.concat([attention_mask[:, :, beg_q:end_q], mem_mask], dim=-1)
+                        assert attn_mask.shape[-1] == attn_keys.shape[-2]
+                    else:
+                        attn_keys = key_states[:, beg_h:end_h]
+                        attn_values = value_states[:, beg_h:end_h]
+                        attn_mask = attention_mask[:, :, beg_q:end_q]
+
+                    attn_queries = query_states[:, beg_h:end_h, beg_q:end_q]
+
+                    attn_output = torch.nn.functional.scaled_dot_product_attention(
+                        query=attn_queries, key=attn_keys, value=attn_values, attn_mask=attn_mask
+                    )
+                    attn_output_q.append(attn_output)
+                else:
+                    attn_weights = torch.matmul(
+                        query_states[:, beg_h:end_h, beg_q:end_q], key_states[:, beg_h:end_h].transpose(2, 3)
+                    ) / math.sqrt(self.head_dim)
+
+                    if attn_weights.size() != (bsz, end_h - beg_h, end_q - beg_q, kv_seq_len):
+                        raise ValueError(
+                            f"Attention weights should be of size {(bsz, end_h - beg_h, end_q - beg_q, kv_seq_len)}, but is"
+                            f" {attn_weights.size()}"
+                        )
+
+                    if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                        raise ValueError(
+                            f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                        )
+                    attn_weights = attn_weights + attention_mask[:, :, beg_q:end_q]
+                    min_value = (
+                        torch.finfo(attn_weights.dtype).min
+                        if -1000000.0 < torch.finfo(attn_weights.dtype).min
+                        else -1000000.0
+                    )
+                    attn_weights = torch.max(
+                        attn_weights, torch.tensor(min_value, device=attn_weights.device, dtype=attn_weights.dtype)
+                    )
+
+                    if mem_cache is not None:
+                        mem_mask = mem_cache.masks.squeeze(-1).unsqueeze(-2)
+                        mem_attn_weights = torch.matmul(
+                            query_states[:, beg_h:end_h, beg_q:end_q],
+                            mem_cache.keys[:, beg_h:end_h].transpose(2, 3).to(key_states.dtype),
+                        ) / math.sqrt(self.head_dim)
+
+                        assert mem_mask.shape[2] == 1
+                        mem_attn_weights = mem_attn_weights + mem_mask
+                        min_value = (
+                            torch.finfo(mem_attn_weights.dtype).min
+                            if -1000000.0 < torch.finfo(mem_attn_weights.dtype).min
+                            else -1000000.0
+                        )
+                        mem_attn_weights = torch.max(
+                            mem_attn_weights,
+                            torch.tensor(min_value, device=mem_attn_weights.device, dtype=mem_attn_weights.dtype),
+                        )
+
+                        attn_weights = torch.concat([attn_weights, mem_attn_weights], dim=-1)
+                        combined_value_states = torch.concat(
+                            [value_states[:, beg_h:end_h], mem_cache.values[:, beg_h:end_h].to(value_states.dtype)],
+                            dim=-2,
+                        )
+                    else:
+                        combined_value_states = value_states[:, beg_h:end_h]
+                    # 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, combined_value_states)
+                    assert attn_output.shape[-2] == end_q - beg_q
+                    attn_output_q.append(attn_output)
+            attn_output_h.append(torch.concat(attn_output_q, dim=-2))
+
+        attn_output = torch.concat(attn_output_h, dim=-3)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        if mem_no_local_cache:
+            if mem_cache is not None:
+                mem_cache = mem_apply_update(
+                    prev_mem_cache=mem_cache, new_mem_content=mem_update, mem_config=self.mem_config
+                )
+            else:
+                mem_cache = mem_update
+
+        return attn_output, attn_weights, past_key_value, mem_cache
+
+
+# Modified transformers.models.llama.modeling_llama.LlamaDecoderLayer
+class LongLlamaDecoderLayer(nn.Module):
+    def __init__(self, config: LongLlamaConfig, mem_config: Optional[LongLlamaMemConfig] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LongLlamaAttention(config=config, mem_config=mem_config)
+        self.mlp = LongLlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = LongLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LongLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        mem_cache: Optional[LongLlamaMemCache] = None,
+    ) -> 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.
+            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`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+                along with information about positions
+            mem_cache (`LongLlamaMemCache`, *optional*): memory cache for specific layers
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value, mem_cache = self.self_attn(
+            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,
+            mem_cache=mem_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs + (mem_cache,)
+
+
+LONGLLAMA_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 ([`LongLlamaConfig`]):
+            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.
+"""
+LONGLLAMA_MEML_DOCSTRING = r"""
+        mem_layers ([`int`], *optional*):
+            Indices of layers to be augmented with memory, if None then parameters from config will be used
+        mem_dtype (`str`, *optional*):
+            Keys and values will be casted to this type for storage.
+
+"""
+
+
+@add_start_docstrings(
+    "The bare LongLLaMA Model outputting raw hidden-states without any specific head on top.",
+    LONGLLAMA_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->LongLlama
+class LongLlamaPreTrainedModel(PreTrainedModel):
+    config_class = LongLlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LongLlamaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            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=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LongLlamaModel):
+            module.gradient_checkpointing = value
+
+
+LONGLLAMA_COMMON_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.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`
+            or memory cache is enabled):
+            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)`) and 1 additional tensor of shape
+            `(batch_size, 1, sequence_length, 1)`. For memory enriched layers it also contains content of memory cache.
+            It is padded with empty tensors so when returned it alwyas has 6 elements.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) 
+            that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_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. This is NOT supported in LongLlamaForCausalLM and LongLlamaForSequenceClassification
+            due to the specific input processing.
+        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.
+"""
+LONGLLAMA_MODEL_INPUTS_DOCSTRING = r"""
+        mem_caches (`tuple(LongLlamaMemCache)`, *optional*) 
+            Memory caches for specified layers, None for others
+"""
+
+LONGLLAMA_ADD_INPUTS_DOCSTRING = r"""
+        last_context_length (`int`, *optional*) 
+            Useful for generation, specifies number of tokens that won't be loaded to memory and 
+            will be left for generation cache
+"""
+
+
+def _prepare_pos_ids(past_key_values, batch_size, input_length, device):
+    if past_key_values is not None:
+        # take previous max pos_id + 1
+        if past_key_values[0][2].shape[0] != batch_size:
+            raise ValueError(
+                f"first dimension of past_key_values should match batch size: {batch_size}"
+                f"but got {past_key_values[0][2].shape[0]}"
+            )
+        next_pos = torch.max(past_key_values[0][2].view(batch_size, -1), dim=-1)[0] + 1
+        next_pos = next_pos.view(batch_size, 1)
+    else:
+        next_pos = torch.zeros(batch_size, 1, device=device, dtype=torch.long)
+
+    position_ids = torch.arange(0, input_length, dtype=torch.long, device=device).view(1, input_length)
+    position_ids = position_ids + next_pos
+    return position_ids
+
+
+@add_start_docstrings(
+    "The bare LongLLaMA Model outputting raw hidden-states without any specific head on top.",
+    LONGLLAMA_START_DOCSTRING,
+    LONGLLAMA_MEML_DOCSTRING,
+)
+# Modified transformers.models.llama.modeling_llama.LlamaModel
+class LongLlamaModel(LongLlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LongLlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LongLlamaConfig):
+        super().__init__(config)
+        self.mem_layers = config.mem_layers
+        self.mem_config = LongLlamaMemConfig(
+            positionals=config.mem_positionals,
+            cache_dtype=getattr(torch, config.mem_dtype),
+            attention_grouping=config.mem_attention_grouping,
+        )
+        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)
+
+        for mem_layer_id in self.mem_layers:
+            if mem_layer_id < 0 or mem_layer_id >= config.num_hidden_layers:
+                raise ValueError(
+                    f"Memory layer ids should be between 0 and {config.num_hidden_layers}, got {mem_layer_id}"
+                )
+
+        layers = []
+        for layer_id in range(config.num_hidden_layers):
+            if layer_id in self.mem_layers:
+                layer = LongLlamaDecoderLayer(config, mem_config=self.mem_config)
+            else:
+                layer = LongLlamaDecoderLayer(config, mem_config=None)
+            layers.append(layer)
+
+        self.layers = nn.ModuleList(layers)
+        self.norm = LongLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(LONGLLAMA_COMMON_INPUTS_DOCSTRING, LONGLLAMA_MODEL_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,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        mem_caches: Optional[Tuple[Optional[LongLlamaMemCache]]] = None,
+    ) -> Union[Tuple, LongLlamaModelOutputWithPast]:
+        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
+        )
+        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
+        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")
+        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")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[-2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = _prepare_pos_ids(past_key_values, batch_size, seq_length, device)
+        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 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
+
+        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
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = ()
+        next_mem_caches = ()
+        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
+            mem_cache = mem_caches[idx] if mem_caches else None
+
+            if mem_cache is not None and idx not in self.mem_layers:
+                raise ValueError("Memory cache provided for a non-memory leayer")
+
+            if (
+                self.gradient_checkpointing
+                and self.training
+                and mem_cache is None
+                and idx % self.config.gradient_checkpoint_every_ith == 0
+            ):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None, mem_cache=None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    mem_cache=mem_cache,
+                )
+
+            new_mem_cache = layer_outputs[-1]
+            layer_outputs = layer_outputs[:-1]
+            next_mem_caches += (new_mem_cache,)
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+            else:
+                next_decoder_cache += (None,)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # 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
+
+        mem_cache_returned = False
+        for mem_cache in next_mem_caches:
+            if mem_cache is not None:
+                mem_cache_returned = True
+        next_mem_caches = next_mem_caches if mem_cache_returned else None
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, next_mem_caches]
+                if v is not None
+            )
+        return LongLlamaModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            mem_caches=next_mem_caches,
+        )
+
+
+def _handle_output_of_past_key_values(outputs):
+    # merges local caches and memory caches into one single tuple of past_key_values
+    # in order to support generation
+    batch_size = outputs.last_hidden_state.shape[0]
+    if outputs.past_key_values is None and outputs.mem_caches is None:
+        return None
+
+    if outputs.past_key_values is None:
+        out_past_key_values = (None,) * len(outputs.mem_caches)
+    else:
+        out_past_key_values = outputs.past_key_values
+
+    if outputs.mem_caches is None:
+        out_mem_caches = (None,) * len(outputs.past_key_values)
+    else:
+        out_mem_caches = outputs.mem_caches
+
+    device = outputs.last_hidden_state.device
+    past_key_values = ()
+    for local_cache, mem_cache in zip(out_past_key_values, out_mem_caches):
+        layer = ()
+        if local_cache is not None:
+            assert len(local_cache) == 3
+            layer += local_cache
+        else:
+            layer += (torch.empty(batch_size, 0, 0, 0, device=device),) * 3
+
+        if mem_cache is not None:
+            layer += (mem_cache.keys, mem_cache.values, mem_cache.masks)
+        else:
+            layer += (torch.empty(batch_size, 0, 0, 0, device=device),) * 3
+
+        assert len(layer) == 6
+
+        past_key_values += (layer,)
+
+    return past_key_values
+
+
+def _split_past_key_values(past_key_values):
+    # splits past_key_values to local cache and memory cache
+    local_cache_preset = False
+    mem_caches_present = False
+    if past_key_values is not None:
+        local_caches = ()
+        mem_caches = ()
+        for layer in past_key_values:
+            if len(layer) != 6:
+                raise ValueError(
+                    "Expected elements of past_key_values to contain 6 elements."
+                    "First 3 describing local cache and last 3 describing memory cache."
+                    f"Instead got {len(layer)} elements"
+                )
+            else:
+                lk, lv, li, memk, memv, memm = layer
+                if lk.shape[-2] != 0:
+                    local_cache_preset = True
+                    local_caches += ((lk, lv, li),)
+                else:
+                    local_caches += (None,)
+
+                if memk.shape[-2] != 0:
+                    mem_caches_present = True
+                    mem_caches += (LongLlamaMemCache(keys=memk, values=memv, masks=memm),)
+                else:
+                    mem_caches += (None,)
+
+    local_caches = local_caches if local_cache_preset else None
+    mem_caches = mem_caches if mem_caches_present else None
+
+    return local_caches, mem_caches
+
+
+def _handle_long_input(
+    model,
+    input_ids,
+    attention_mask,
+    position_ids,
+    past_key_values,
+    inputs_embeds,
+    use_cache,
+    output_attentions,
+    output_hidden_states,
+    return_dict,
+    context_window_length,
+    last_context_length,
+):
+    if output_attentions:
+        logger.warning(
+            f"Outputing attentions is not supported in LongLlamaForCausalLM and LongLlamaForSequenceClassification. "
+            f"Attention of the last window will be returned"
+        )
+
+    past_key_values, mem_caches = _split_past_key_values(past_key_values)
+
+    if past_key_values is not None and use_cache is False:
+        raise ValueError("past_key_values it not None should imply use_cache == True")
+
+    if past_key_values is not None:
+        initial_past_key_values_length = past_key_values[0][0].shape[-2]
+    else:
+        initial_past_key_values_length = 0
+
+    if input_ids is not None:
+        batch_size, input_length = input_ids.shape
+    else:
+        batch_size, input_length, _ = inputs_embeds.shape
+
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = _prepare_pos_ids(past_key_values, batch_size, input_length, device)
+
+    if position_ids.shape != (batch_size, input_length):
+        raise ValueError(f"Shape of position_ids [{position_ids}] should match [{batch_size, input_length}]")
+
+    if attention_mask is not None:
+        attention_mask = attention_mask[..., -(initial_past_key_values_length + input_length) :]
+        if attention_mask is not None and (
+            attention_mask.shape != (batch_size, initial_past_key_values_length + input_length)
+        ):
+            raise ValueError(
+                "Attention mask should be provided for both the local cache and the input",
+                f"Expected shape {(batch_size, initial_past_key_values_length + input_length)},"
+                f"got {attention_mask.shape}.",
+            )
+
+    # First we load prefix to memory cache
+    mem_input_length = max(input_length - last_context_length, 0)
+    outputs_list = []
+    attn_offset = initial_past_key_values_length
+    if mem_input_length > 0:
+        for i in range(0, mem_input_length, context_window_length):
+            beg, end = i, min(mem_input_length, i + context_window_length)
+
+            if attention_mask is not None:
+                if past_key_values is not None:
+                    local_cache_size = past_key_values[0][0].shape[-2]
+                else:
+                    local_cache_size = 0
+                attn_length = attention_mask.shape[-1]
+                attn_beg = beg + attn_offset - local_cache_size
+                attn_end = end + attn_offset
+                assert attn_end <= attn_length
+                assert attn_beg >= 0 and attn_end > attn_beg
+
+            # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn, mem_caches)
+            outputs = model(
+                input_ids=input_ids[..., beg:end] if input_ids is not None else None,
+                attention_mask=attention_mask[..., attn_beg:attn_end] if attention_mask is not None else None,
+                position_ids=position_ids[..., beg:end],
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds[..., beg:end, :] if inputs_embeds is not None else None,
+                use_cache=False if past_key_values is None else use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=True,
+                mem_caches=mem_caches,
+            )
+            if i > 0:
+                if mem_caches is not None and past_key_values is None:
+                    for mc_layer in mem_caches:
+                        if mc_layer is not None:
+                            del mc_layer.keys
+                            del mc_layer.values
+                            del mc_layer.masks
+
+            mem_caches = outputs.mem_caches
+            outputs.mem_caches = None
+            past_key_values = outputs.past_key_values
+            outputs.past_key_values = None
+            outputs_list.append(outputs)
+
+    remaining_input_length = input_length - mem_input_length
+    beg = mem_input_length
+    attn_length = remaining_input_length
+    if past_key_values is not None:
+        attn_length += past_key_values[0][0].shape[-2]
+    attention_mask = attention_mask[..., -attn_length:] if attention_mask is not None else None
+
+    outputs = model(
+        input_ids=input_ids[..., beg:] if input_ids is not None else None,
+        attention_mask=attention_mask,
+        position_ids=position_ids[..., beg:],
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds[..., beg:, :] if inputs_embeds is not None else None,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=True,
+        mem_caches=mem_caches,
+    )
+
+    outputs_list.append(outputs)
+
+    past_key_values = _handle_output_of_past_key_values(outputs_list[-1])
+
+    if output_hidden_states:
+        hidden_states = ()
+        for hd in zip(*[x.hidden_states for x in outputs_list]):
+            hidden_states += (torch.cat(hd, dim=-2),)
+    else:
+        hidden_states = None
+
+    outputs = BaseModelOutputWithPast(
+        last_hidden_state=torch.concat([x.last_hidden_state for x in outputs_list], dim=-2),
+        past_key_values=past_key_values,
+        hidden_states=hidden_states,
+        attentions=outputs_list[-1].attentions,
+    )
+
+    if not return_dict:
+        outputs = tuple(
+            v
+            for v in [outputs.last_hidden_state, outputs.past_key_values, outputs.hidden_states, outputs.attentions]
+            if v is not None
+        )
+    return outputs
+
+
+# Modified transformers.models.llama.modeling_llama.LlamaForCausalLM
+class LongLlamaForCausalLM(LongLlamaPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.context_window_length = config.max_position_embeddings
+
+        self.model = LongLlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, 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
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def _has_generation_cache(self, past_key_values):
+        if past_key_values is not None:
+            assert len(past_key_values[0]) == 6
+            return past_key_values[0][0].shape[-2] != 0
+
+        return False
+
+    @add_start_docstrings_to_model_forward(LONGLLAMA_COMMON_INPUTS_DOCSTRING, LONGLLAMA_ADD_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        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,
+        last_context_length: Optional[int] = 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, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> 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]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        last_context_length = (
+            last_context_length if last_context_length is not None else self.config.last_context_length
+        )
+        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
+
+        outputs = _handle_long_input(
+            model=self.model,
+            input_ids=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,
+            context_window_length=self.context_window_length,
+            last_context_length=last_context_length,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        last_context_length=None,
+        **kwargs,
+    ):
+        if self._has_generation_cache(past_key_values):
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill(position_ids < 0, 0)
+            if self._has_generation_cache(past_key_values):
+                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 is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "last_context_length": last_context_length,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The LongLLaMA Model transformer with a sequence classification head on top (linear layer).
+
+    [`LongLlamaForSequenceClassification`] 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).
+    """,
+    LONGLLAMA_START_DOCSTRING,
+    LONGLLAMA_MEML_DOCSTRING,
+)
+# Modified from transformers.models.llama.modeling_llama.LlamaForSequenceClassification
+class LongLlamaForSequenceClassification(LongLlamaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.context_window_length = config.max_position_embeddings
+        self.model = LongLlamaModel(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(LONGLLAMA_COMMON_INPUTS_DOCSTRING, LONGLLAMA_ADD_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,
+        last_context_length: Optional[int] = 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).
+        """
+        last_context_length = (
+            last_context_length if last_context_length is not None else self.config.last_context_length
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        transformer_outputs = _handle_long_input(
+            model=self.model,
+            input_ids=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,
+            context_window_length=self.context_window_length,
+            last_context_length=last_context_length,
+        )
+
+        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:
+                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).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,
+        )

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