updated readme

README.md

@@ -1,3 +1,13 @@
 ---
 license: other
 ---
+WizardLM-7B with 10k+ context using Landmark Attention.
+
+Model generated using Landmark-Attention-QLoRA
+https://github.com/eugenepentland/landmark-attention-qlora
+
+A merge of the following models:
+https://huggingface.co/TheBloke/wizardLM-7B-HF
+https://huggingface.co/eugenepentland/WizardLM-7B-Landmark-Attention-QLoRA
+
+Can be loaded in using oobooga, make sure to have the --trust-remote-code option on for it to function.

config.json

@@ -3,6 +3,12 @@
   "architectures": [
     "LlamaForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration_landmark_llama.LlamaConfig",
+    "AutoModel": "modelling_landmark_llama.LlamaModel",
+    "AutoModelForCausalLM": "modelling_landmark_llama.LlamaForCausalLM",
+    "AutoModelForSequenceClassification": "modelling_landmark_llama.LlamaForSequenceClassification"
+  },
   "bos_token_id": 1,
   "eos_token_id": 2,
   "hidden_act": "silu",

configuration_landmark_llama.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+# Copyright 2022 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.
+""" LLaMA model configuration"""
+
+from transformers import LlamaConfig as HFLlamaConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlamaConfig(HFLlamaConfig):
+    model_type = "llama"
+
+    def __init__(
+        self,
+        mem_id=32001,
+        mem_freq=50,
+        mem_top_k=5,
+        mem_max_seq_len=255,
+        mem_max_cache_size=None,
+        **kwargs,
+    ):
+        self.mem_id = mem_id
+        self.mem_freq = mem_freq
+        self.mem_top_k = mem_top_k
+        self.mem_max_seq_len = mem_max_seq_len
+        self.mem_max_cache_size = mem_max_cache_size
+        super().__init__(**kwargs)

modelling_landmark_llama.py

@@ -0,0 +1,1217 @@
+# coding=utf-8
+# Copyright 2022 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 LLaMA model."""
+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 transformers.models.llama.configuration_llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# 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.tensor(torch.finfo(dtype).min, device=device), 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)
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        # convert into half-precision if necessary
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            hidden_states = hidden_states.to(self.weight.dtype)
+
+        return self.weight * hidden_states
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        # Build here to make `torch.jit.trace` work.
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.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, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(self.max_seq_len_cached, device=x.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).to(x.device)
+            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+        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)
+
+
+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)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    if q is None:
+        q_embed = None
+    else:
+        q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaMLP(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))
+
+class LandmarkGroupedSoftmaxFunction(torch.autograd.Function):
+
+    # Note that forward, setup_context, and backward are @staticmethods
+    @staticmethod
+    def forward(ctx, x, dim, mem_cnt, resp_mem_idx):
+        new_shape = list(x.shape)
+        new_shape[dim] = mem_cnt # max_mem_cnt.item()
+        max_by_group = x.new_zeros((*new_shape,))
+        max_by_group.scatter_reduce_(src=x, index=resp_mem_idx, dim=dim, reduce="amax", include_self=False)
+            
+        maxes = torch.gather(max_by_group, dim, resp_mem_idx)
+        #x_exp = torch.exp(x - torch.where(torch.isinf(maxes), 0, maxes))
+        x_exp = torch.exp((x - maxes).to(torch.float32))
+
+        cumsum_by_group = torch.zeros_like(max_by_group, dtype=x_exp.dtype)
+
+        cumsum_by_group.scatter_add_(dim, resp_mem_idx, x_exp, )
+        denom = torch.gather(cumsum_by_group, dim, resp_mem_idx)
+
+        #probs = torch.where(denom < 0.5, 0, x_exp / denom)
+        probs = x_exp / denom
+        
+        
+        ctx.mem_cnt = mem_cnt
+        ctx.dim = dim
+        ctx.save_for_backward(resp_mem_idx, probs)
+
+        return probs
+
+    @staticmethod
+    def backward(ctx, grad_probs):
+        mem_cnt = ctx.mem_cnt
+        dim = ctx.dim
+        resp_mem_idx, probs = ctx.saved_tensors
+        grad_x = grad_dim = grad_mem_cnt = grad_resp_mem_idx = None
+
+        if ctx.needs_input_grad[0] or ctx.needs_input_grad[4]:
+            grad_pair = grad_probs * probs
+
+            new_shape = list(probs.shape)
+            new_shape[dim] = mem_cnt # max_mem_cnt.item()
+            cumsum_by_group = grad_pair.new_zeros((*new_shape,))
+            cumsum_by_group.scatter_add_(dim, resp_mem_idx, grad_pair)
+
+
+        if ctx.needs_input_grad[0]:
+            grad_sum = torch.gather(cumsum_by_group, dim, resp_mem_idx)
+            grad_x = grad_pair - probs * grad_sum
+        assert not ctx.needs_input_grad[1]
+        assert not ctx.needs_input_grad[2]
+        assert not ctx.needs_input_grad[3]
+        
+        return grad_x, grad_dim, grad_mem_cnt, grad_resp_mem_idx
+
+def landmark_grouped_softmax(x, dim, is_mem, last_section_mask):
+    
+    last_and_rest_mask = last_section_mask # | mask
+
+    full_access_mask =  is_mem | last_and_rest_mask
+
+    max_mem_cnt = 16
+    mem_group_idx = torch.cumsum(is_mem, dim=dim)
+    mem_bucket_id = max_mem_cnt - 1
+    resp_mem_idx = torch.where(last_and_rest_mask, 
+                                max_mem_cnt - 1,
+                                torch.where(is_mem, mem_bucket_id, mem_group_idx))
+    probs = LandmarkGroupedSoftmaxFunction.apply(x, dim, max_mem_cnt, resp_mem_idx)
+
+    new_shape = list(x.shape)
+    new_shape[dim] = max_mem_cnt
+    group_prob = probs.new_zeros((*new_shape, ))
+    group_prob.scatter_(dim, torch.where(is_mem, mem_group_idx - 1, max_mem_cnt - 1), probs)
+    probs = probs.mul(torch.where(full_access_mask, last_section_mask, torch.gather(group_prob, dim, resp_mem_idx)))
+
+
+    return probs
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: LlamaConfig):
+        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
+
+        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.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+        self.mem_freq = None
+        self.top_k = None
+        self.max_cache_size = None
+
+    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 set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
+        self.mem_freq = mem_freq
+        self.top_k = top_k
+        self.max_cache_size = max_cache_size
+
+    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,
+        is_mem: Optional[torch.Tensor] = None,
+        last_section_mask: Optional[torch.Tensor] = None,
+        offload_cache_to_cpu: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+            if len(past_key_value) > 2:
+                kv_seq_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        key_states_before_pos = key_states
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        # [bsz, nh, t, hd]
+
+        attn_prefix = None
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            if self.mem_freq is None:
+                cache_len = past_key_value[0].shape[2]
+                if self.max_cache_size is not None:
+                    cache_len = min(cache_len, self.max_cache_size)
+                if is_mem is not None:
+                    is_mem = torch.cat((is_mem.new_zeros((1, 1, q_len, cache_len)), is_mem), dim=-1)
+                    last_section_mask = torch.cat((last_section_mask.new_ones((1, 1, q_len, cache_len)), last_section_mask), dim=-1)
+
+                past_key_states = torch.cat([past_key_value[0], key_states], dim=2)
+                past_value_states = torch.cat([past_key_value[1], value_states], dim=2)
+                key_states = past_key_states[:, :, -(q_len + cache_len):]
+                value_states = past_value_states[:, :, -(q_len + cache_len):]
+                expected_att_size = (bsz, self.num_heads, q_len, cache_len + q_len)
+            else:
+                orig_value_states = value_states
+
+                incomplete_len = past_key_value[0].shape[2] % (self.mem_freq + 1)
+                full_len = past_key_value[0].shape[2] - incomplete_len
+                past_key_mem, past_key_incomplete = torch.split(past_key_value[0], (full_len, incomplete_len), dim=2)
+                past_value_mem, past_value_incomplete = torch.split(past_key_value[1], (full_len, incomplete_len), dim=2)
+
+                if offload_cache_to_cpu:
+                    past_key_value = (past_key_incomplete, past_value_incomplete, *past_key_value[2:])
+                
+                if incomplete_len > 0:
+                    assert q_len + incomplete_len <= (self.mem_freq + 1)
+                is_mem = torch.cat((is_mem.new_zeros((1, 1, q_len, incomplete_len)), is_mem), dim=-1)
+                last_section_mask = torch.cat((last_section_mask.new_ones((1, 1, q_len, incomplete_len)), last_section_mask), dim=-1)
+
+                if len(past_key_value) > 2:
+                    full_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
+                past_key_incomplete_pos = torch.arange(full_len, full_len + incomplete_len, dtype=torch.long, device=position_ids.device).unsqueeze(0)
+                _, past_key_incomplete = apply_rotary_pos_emb(None, past_key_incomplete, cos, sin, past_key_incomplete_pos)
+                key_states = torch.cat((past_key_incomplete, key_states), dim=2)
+                value_states = torch.cat((past_value_incomplete, value_states), dim=2)
+
+                past_key_mem = past_key_mem.view(bsz, self.num_heads, -1, self.mem_freq + 1, self.head_dim)
+                past_value_mem = past_value_mem.view(bsz, self.num_heads, -1, self.mem_freq + 1, self.head_dim)
+
+                if len(past_key_value) > 2:
+                    mem_key_nopos = torch.cat((
+                        past_key_value[2], 
+                        past_key_mem.select(dim=3, index=self.mem_freq)), dim=2)
+                    past_key_mem_offload = past_key_value[3]
+                    past_key_mem = torch.cat((
+                        past_key_mem_offload, 
+                        past_key_mem.to(past_key_mem_offload.device)), dim=2)
+                    past_value_mem = torch.cat((past_key_value[4], past_value_mem.to(past_key_mem_offload.device)), dim=2)
+                else:
+                    mem_key_nopos = past_key_mem.select(dim=3, index=self.mem_freq)    
+                
+                num_mems = past_key_mem.shape[2]
+                top_k = min(self.top_k, num_mems)
+                prefix_len = full_len - (top_k + 1) * (self.mem_freq + 1)
+                mem_indices = torch.cat(
+                    (position_ids.new_zeros((max(0, num_mems - top_k), )),
+                    torch.arange(1, top_k + 1, device=query_states.device, dtype=position_ids.dtype)), dim=0)
+                mem_pos = (mem_indices * (self.mem_freq + 1) + self.mem_freq).unsqueeze(0).expand(bsz, -1) + prefix_len
+                _, mem_key = apply_rotary_pos_emb(None, mem_key_nopos, cos, sin, mem_pos)
+                mem_attn_weights = torch.matmul(query_states, mem_key.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+                if offload_cache_to_cpu:
+                    aggregate = "max_over_tokens"
+                else:
+                    aggregate = None
+                if aggregate == "max_over_tokens":
+                    token_retrievers = 1
+                    head_retrievers = self.num_heads
+                    mem_attn_weights = torch.nn.functional.softmax(mem_attn_weights, dim=-1)
+                    mem_attn_weights = mem_attn_weights.amax(dim=2, keepdim=True)
+                elif aggregate is None:
+                    token_retrievers = q_len
+                    head_retrievers = self.num_heads
+                else:
+                    raise NotImplementedError()
+
+                mem_selected_idx = mem_attn_weights.topk(dim=-1,k=top_k)[1].sort(dim=-1)[0].view(bsz, head_retrievers, token_retrievers, top_k)
+                
+                selected_indices = torch.arange(0, top_k * (self.mem_freq + 1), device=query_states.device, dtype=position_ids.dtype)
+                selected_indices = torch.where(mem_selected_idx >= num_mems - top_k, self.mem_freq + 1, 0).unsqueeze(-1) + selected_indices.view(1, 1, 1, top_k, self.mem_freq + 1)
+                selected_indices = selected_indices.view(bsz, head_retrievers, token_retrievers, -1).expand(bsz, self.num_heads, q_len, -1) + prefix_len
+
+                
+                
+                
+                mem_selected_idx = mem_selected_idx.to(past_key_mem.device)
+                
+                mem_selected_idx = mem_selected_idx.view(bsz, self.num_heads, token_retrievers, top_k, 1, 1).expand(bsz, self.num_heads, token_retrievers, top_k, self.mem_freq + 1, self.head_dim) 
+                selected_keys = past_key_mem.unsqueeze(2).expand(bsz, self.num_heads, token_retrievers, -1, self.mem_freq + 1, self.head_dim)
+                selected_keys = selected_keys.take_along_dim(mem_selected_idx, dim=3).to(query_states.device)
+                selected_values = past_value_mem.unsqueeze(2).expand(bsz, self.num_heads, token_retrievers, -1, self.mem_freq + 1, self.head_dim).take_along_dim(mem_selected_idx, dim=3).to(query_states.device)
+            
+                selected_keys = selected_keys.view(bsz, self.num_heads, token_retrievers, -1, self.head_dim).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
+                selected_keys = apply_rotary_pos_emb(None, selected_keys.unsqueeze(1), cos, sin, selected_indices)[1].squeeze(1)    
+                selected_values = selected_values.view(bsz, self.num_heads, token_retrievers, -1, self.head_dim).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
+                attn_prefix = torch.matmul(query_states.unsqueeze(3), selected_keys.transpose(3, 4)).squeeze(3) / math.sqrt(self.head_dim)
+                is_mem_prefix = torch.cat((is_mem.new_zeros((self.mem_freq, )), is_mem.new_ones((1, )))).unsqueeze(0).repeat((top_k, 1))
+                is_mem_prefix = is_mem_prefix.view(1, 1, 1, -1).expand(1, 1, q_len, -1)
+                is_mem = torch.cat((is_mem_prefix, is_mem), dim=-1)
+                last_section_mask = torch.cat((last_section_mask.new_zeros((1, 1, q_len, top_k * (self.mem_freq + 1))), last_section_mask), dim=-1)
+                expected_att_size = (bsz, self.num_heads, q_len, q_len + incomplete_len)
+
+                past_key_states = torch.cat([past_key_value[0], key_states_before_pos], dim=2)
+                past_value_states = torch.cat([past_key_value[1], orig_value_states], dim=2)
+
+                if offload_cache_to_cpu:
+                    past_key_value = (past_key_states, past_value_states, mem_key_nopos, past_key_mem.to("cpu"), past_value_mem.to("cpu"), *past_key_value[5:]) if use_cache else None
+                else:
+                    past_key_value = (past_key_states, past_value_states) if use_cache else None
+
+        else:
+            if self.mem_freq is None:
+                past_key_states = key_states
+            else:
+                past_key_states = key_states_before_pos
+            past_value_states = value_states
+            expected_att_size = (bsz, self.num_heads, q_len, kv_seq_len)
+            past_key_value = (past_key_states, past_value_states) if use_cache else None
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if attn_weights.size() != expected_att_size:
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            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[...,-attn_weights.shape[-1]:]
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+        if attn_prefix is not None:
+            attn_weights = torch.cat((attn_prefix, attn_weights), dim=-1)
+        # upcast attention to fp32
+        if is_mem is None:
+            raise ValueError("Don't use this without landmarks")
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        else:
+            attn_weights = landmark_grouped_softmax(attn_weights, dim=-1, is_mem=is_mem.expand(-1, self.num_heads, -1, -1), last_section_mask=last_section_mask).to(query_states.dtype)
+        if attn_prefix is not None:
+            attn_prefix, attn_weights = torch.split(attn_weights, (attn_prefix.shape[-1], attn_weights.shape[-1] - attn_prefix.shape[-1]), dim=-1)
+        attn_output = torch.matmul(attn_weights, value_states)
+        if attn_prefix is not None:
+            attn_output += torch.matmul(attn_prefix.unsqueeze(3), selected_values).squeeze(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
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
+        self.self_attn.set_mem_cache_args(mem_freq, top_k, max_cache_size)
+    
+    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,
+        is_mem: Optional[torch.Tensor] = None,
+        last_section_mask: Optional[torch.Tensor] = None,
+        offload_cache_to_cpu: 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.
+            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
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = 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,
+            is_mem=is_mem,
+            last_section_mask=last_section_mask,
+            offload_cache_to_cpu=offload_cache_to_cpu
+        )
+        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
+
+
+LLAMA_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 ([`LlamaConfig`]):
+            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.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+    config_class = LlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _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, LlamaModel):
+            module.gradient_checkpointing = value
+
+
+LLAMA_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 (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            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 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-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.
+        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 LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        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([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.mem_id = None
+
+        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
+
+    def set_mem_id(self, mem_id):
+        self.mem_id = mem_id
+
+    def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
+        for l in self.layers:
+            l.set_mem_cache_args(mem_freq, top_k, max_cache_size)
+
+    # 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(LLAMA_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,
+        offload_cache_to_cpu: 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
+        )
+        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
+        is_mem = None
+        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
+            if self.mem_id is not None:
+                with torch.no_grad():
+                    is_mem = input_ids == self.mem_id
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+            if self.mem_id is not None:
+                raise NotImplementedError
+        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:
+            if is_mem is not None:
+                pass
+                #raise NotImplementedError
+            past_key_values_length = past_key_values[0][0].shape[2]
+            if len(past_key_values[0]) > 2:
+                past_key_values_length += past_key_values[0][3].shape[2] * past_key_values[0][3].shape[3]
+            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
+            )
+            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 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
+        )
+        
+        last_section_mask = None
+        if is_mem is not None:
+            is_mem = is_mem.unsqueeze(1).unsqueeze(2)
+            current_len = input_ids.shape[1]
+            mem_ids = torch.where(attention_mask[..., -current_len:] < -1, 0, torch.cumsum(is_mem, -1) - is_mem.int())
+            last_section_mask = torch.amax(mem_ids, -1, keepdim=True) == mem_ids
+            attention_mask[..., -current_len:].masked_fill_(last_section_mask & is_mem, torch.tensor(torch.finfo(inputs_embeds.dtype).min, device=inputs_embeds.device))
+            last_section_mask.logical_and_(attention_mask[..., -current_len:] > -1)
+            is_mem = is_mem.logical_and(attention_mask[..., -current_len:] > -1)
+            
+
+        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 = () if use_cache else None
+
+        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:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                    is_mem,
+                    last_section_mask
+                )
+            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,
+                    is_mem=is_mem,
+                    last_section_mask=last_section_mask,
+                    offload_cache_to_cpu=offload_cache_to_cpu
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                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
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        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 BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.set_mem_id(config.mem_id)
+        self.set_mem_cache_args(config.mem_max_seq_len, config.mem_freq, config.mem_top_k, config.mem_max_cache_size)
+
+        # 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
+
+    @add_start_docstrings_to_model_forward(LLAMA_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,
+        offload_cache_to_cpu: 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, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? 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 consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+
+        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
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        window_len = self.max_seq_len or input_ids.shape[1]
+        last_logits = None
+        for step, idx in enumerate(range(0, input_ids.shape[1], window_len)):
+            if idx >= 1:
+                if output_attentions or output_hidden_states:
+                    raise NotImplementedError
+                if not use_cache:
+                    raise NotImplementedError
+            outputs = self.model(
+                input_ids=input_ids[:, idx:idx + window_len],
+                attention_mask=attention_mask[:, :idx + window_len + attention_mask.shape[1] - input_ids.shape[1]] if attention_mask is not None else None,
+                position_ids=position_ids[:, idx:idx + window_len] if position_ids is not None else None,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds[:, idx:idx + window_len] if inputs_embeds is not None else None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                offload_cache_to_cpu=offload_cache_to_cpu,
+            )
+            past_key_values = outputs[1]
+            if last_logits is not None:
+                last_logits = torch.cat((last_logits, outputs[0]), dim=-2)
+            last_logits = outputs[0]
+
+        hidden_states = last_logits
+        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 set_mem_id(self, mem_id):
+        self.mem_id = mem_id
+        self.model.set_mem_id(mem_id)
+    
+    def set_mem_cache_args(self, max_seq_len, mem_freq, top_k, max_cache_size):
+        self.mem_freq = mem_freq
+        self.top_k = top_k
+        self.max_seq_len = max_seq_len
+        if self.max_seq_len is not None:
+            assert self.max_seq_len % (self.mem_freq + 1) == 0
+        self.model.set_mem_cache_args(mem_freq, top_k, max_cache_size)
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        total_len = input_ids.shape[1]
+        if past_key_values:
+            prev_len = input_ids.shape[1] - 1
+        else:
+            prev_len = 0
+
+        position_ids = kwargs.get("position_ids", None)
+
+        if self.mem_freq is not None:
+            if position_ids is not None:
+                raise NotImplementedError
+            T = input_ids.shape[1]
+
+            prev_incomplete_len = prev_len % self.mem_freq
+            prev_complete_len = prev_len - prev_incomplete_len
+            incomplete_len = total_len % self.mem_freq
+            new_full_len = total_len - prev_complete_len - incomplete_len
+
+            prev_input, input_ids_with_mem, input_ids_without_mem = torch.split(input_ids, (prev_complete_len, new_full_len, incomplete_len), dim=-1)
+            
+            bsz, q_len = input_ids.size()
+            input_ids_with_mem = input_ids_with_mem.view(bsz, -1, self.mem_freq)            
+            input_ids_with_mem = torch.cat(
+                (
+                    input_ids_with_mem, 
+                    input_ids_with_mem.new_full((bsz, input_ids_with_mem.shape[1], 1), self.mem_id)
+                ), 
+                dim=-1
+            ).view(bsz, -1)
+            input_ids = torch.cat((prev_input, input_ids_with_mem, input_ids_without_mem), dim=-1)
+            if attention_mask is not None:
+                attention_mask_with_mem, attention_mask_without_mem = torch.split(attention_mask, (prev_complete_len + new_full_len, incomplete_len), dim=-1)
+                attention_mask_with_mem = attention_mask_with_mem.view(bsz, -1, self.mem_freq)
+                attention_mask_with_mem = torch.cat(
+                    (
+                        attention_mask_with_mem, 
+                        attention_mask_with_mem.new_ones((bsz, attention_mask_with_mem.shape[1], 1))
+                    ), 
+                    dim=-1
+                ).view(bsz, -1)
+                attention_mask = torch.cat((attention_mask_with_mem, attention_mask_without_mem), dim=-1)
+            
+            
+        input_ids = input_ids[:, prev_len:]
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            position_ids = position_ids[:, -input_ids.shape[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 and self.mem_freq 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,
+                "offload_cache_to_cpu": kwargs.get("offload_cache_to_cpu")
+            }
+        )
+        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) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`LlamaForSequenceClassification`] 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).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(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(LLAMA_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:
+                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,
+        )