Delete modeling_llama.py

modeling_llama.py

@@ -1,1164 +0,0 @@
-# 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 faiss
-from einops import rearrange
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-import torch.nn.functional as F
-from torch.linalg import vector_norm
-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_llama import ExtendedLlamaConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "ExtendedLlamaConfig"
-
-
-# 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):
-        input_dtype = hidden_states.dtype
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
-        return (self.weight * hidden_states).to(input_dtype)
-
-
-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)
-        dtype = torch.get_default_dtype()
-        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]
-        # 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, :, :].to(x.dtype), persistent=False
-            )
-            self.register_buffer(
-                "sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False
-            )
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-    
-class LlamaDynamicScaledRotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, ntk=False, device=None):
-        super().__init__()
-        self.ntk = ntk
-        self.base = base
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        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)
-        dtype = torch.get_default_dtype()
-        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]
-        # 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
-            if self.ntk:
-                base = self.base * ((self.ntk * seq_len / self.max_position_embeddings) - (self.ntk - 1)) ** (self.dim / (self.dim-2))
-                inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(x.device) / self.dim))
-                self.register_buffer("inv_freq", inv_freq)
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
-            if not self.ntk:
-                t *= self.max_position_embeddings / seq_len
-            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, :, :].to(x.dtype), persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-class LlamaLinearScaledRotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, device=None):
-        super().__init__()
-        self.scale = scale
-        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)
-        t /= self.scale
-        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)
-        dtype = torch.get_default_dtype()
-        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]
-        # 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)
-            t /= self.scale
-            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, :, :].to(x.dtype), persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-class LlamaNTKScaledRotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, alpha=1, device=None):
-        super().__init__()
-        base = base * alpha ** (dim / (dim-2))
-        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)
-        dtype = torch.get_default_dtype()
-        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]
-        # 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, :, :].to(x.dtype), persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), 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]
-
-    s_q = q.size(-2) #Since we apply rotary pos emb after reading from cache, queries may be shorter
-    _q_position_ids = position_ids[:, -s_q:]
-    _q_cos = cos[_q_position_ids].unsqueeze(1)
-    _q_sin = sin[_q_position_ids].unsqueeze(1)
-    q_embed = (q * _q_cos) + (rotate_half(q) * _q_sin)
-
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    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 ExtendedLlamaAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: ExtendedLlamaConfig):
-        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.num_hidden_layers = config.num_hidden_layers
-
-        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)
-
-    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,
-
-        long_range_past_key_value=None,
-        faiss_indexes=None,
-        mask_by_sim=False,
-        sim_threshold=0.0,
-        topk=None,
-        current_layer=None,
-    ) -> 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)
-        if past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else None
-
-        kv_seq_len = key_states.shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-
-        query_states_no_rotary = query_states.clone() # use queries wo positional info for memory retrieval
-
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, position_ids
-        )
-        # [bsz, nh, t, hd]
-        bsz, nh, s_q, hd = query_states.shape
-        s_k = key_states.size(-2)
-
-        attn_weights = torch.matmul(
-            query_states, key_states.transpose(2, 3)
-        ) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            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 long_range_past_key_value is not None or faiss_indexes is not None:
-            if long_range_past_key_value is not None: #manual memories
-
-                k_cache, v_cache = long_range_past_key_value
-                s_cache = k_cache.size(-2)
-
-                k_cache = k_cache.to(key_states.device)
-                v_cache = v_cache.to(key_states.device)
-
-                q_n = query_states_no_rotary/vector_norm(query_states_no_rotary, ord=2, dim=-1, keepdim=True)
-                k_n = k_cache/vector_norm(k_cache, ord=2, dim=-1, keepdim=True)
-            
-                sim = q_n.matmul(k_n.transpose(2,3))
-                if s_cache<topk:
-                    topk = s_cache #number of tokens in cache < topk
-                val, idx = torch.topk(sim, k=topk, dim=-1)
-
-                reshaped_idx = idx.reshape(bsz, nh, s_q * topk)
-
-                cos_m, sin_m = self.rotary_emb(value_states, seq_len=self.max_position_embeddings) # use max pos emb for memories
-                cos_m = cos_m[:,:,-1,...].repeat(1,1,s_q * topk,1)
-                sin_m = sin_m[:,:,-1,...].repeat(1,1,s_q * topk,1)
-            
-                selected_k = k_cache.gather(dim=-2, index=reshaped_idx.unsqueeze(-1).expand(-1, -1, -1, hd))
-                _, selected_k = apply_rotary_pos_emb(
-                    torch.ones(selected_k.shape, device=key_states.device), selected_k, cos_m, sin_m, position_ids=torch.arange(s_q * topk, device=key_states.device).unsqueeze(0)
-                ) # Apply rotary pos emb to selected memory keys, use dummy input for queries 
-
-                selected_v = v_cache.gather(dim=-2, index=reshaped_idx.unsqueeze(-1).expand(-1, -1, -1, hd))
-
-                sim_mask = rearrange(~ (val > sim_threshold).bool(), 'b h s i -> b h (s i)').unsqueeze(-2).expand(-1, -1, s_q, -1)
-
-            elif faiss_indexes is not None: #faiss indexes
-    
-                kn_index, kv_index = faiss_indexes
-                q_n = query_states_no_rotary/vector_norm(query_states_no_rotary, ord=2, dim=-1, keepdim=True)
-
-                one_hot_encodings = F.one_hot(torch.arange(0, nh*self.num_hidden_layers, device=query_states.device))*10
-                q_n = torch.concat([rearrange(q_n, 'b h s d -> b (h s) d', h=nh), one_hot_encodings[nh*current_layer:nh*(current_layer+1)].unsqueeze(0).repeat_interleave(repeats=query_states.size(-2), dim=-2)], dim=-1).squeeze()
-
-                D, I = kn_index.search(q_n.to('cpu').numpy(), k=topk)
-
-                selected_k=rearrange(torch.tensor(kv_index.reconstruct_batch(I.flatten()))[:,:hd], '(h s) d -> 1 h s d', h=nh).to(query_states.device)
-                cos_m, sin_m = self.rotary_emb(value_states, seq_len=self.max_position_embeddings) # use max pos emb for memories
-                cos_m = cos_m[:,:,-1,...].repeat(1,1,s_q * topk,1)
-                sin_m = sin_m[:,:,-1,...].repeat(1,1,s_q * topk,1)
-            
-                _, selected_k = apply_rotary_pos_emb(
-                    torch.ones(selected_k.shape, device=key_states.device), selected_k, cos_m, sin_m, position_ids=torch.arange(s_q * topk, device=key_states.device).unsqueeze(0)
-                ) # Apply rotary pos emb to selected memory keys, use dummy input for queries 
-
-                selected_v=rearrange(torch.tensor(kv_index.reconstruct_batch(I.flatten()))[:,hd:], '(h s) d -> 1 h s d', h=nh).to(query_states.device)
-            
-            attn_weight_cache = torch.matmul(query_states, selected_k.transpose(2, 3)) / math.sqrt(self.head_dim)
-            if mask_by_sim:
-                attn_weight_cache = attn_weight_cache.masked_fill(sim_mask, torch.finfo(selected_k.dtype).min)
-
-            attn_weights = torch.cat([attn_weight_cache, attn_weights], dim=-1)
-            value_states = torch.cat([selected_v, value_states], dim=-2)
-
-        min_val = torch.finfo(attn_weights.dtype).min
-        def _create_active_externalism_mask(k, s_q, device, min_val=min_val):
-            mask = torch.ones(s_q, s_q * k, device=device, dtype=torch.float32)
-            for i in range(s_q):
-                mask[i, i * k : (i + 1) * k] = 0
-
-            filled = mask.masked_fill(mask.bool(), min_val)
-            return filled
- 
-        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()}"
-                )
-            if long_range_past_key_value is not None:
-                memory_mask = _create_active_externalism_mask(k=topk,s_q=s_q, device=attn_weights.device)
-                attention_mask = torch.cat([memory_mask, attention_mask[:,:,:,-s_k:].squeeze(dim=[0,1])], dim=1).unsqueeze(dim=0).unsqueeze(dim=1)
-
-            attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(
-                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
-            )
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            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 long_range_past_key_value is None and faiss_indexes is None:
-            reshaped_idx=None
-
-        return attn_output, attn_weights, past_key_value, reshaped_idx
-
-class ExtendedLlamaDecoderLayer(nn.Module):
-    def __init__(self, config: ExtendedLlamaConfig):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = ExtendedLlamaAttention(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 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,
-
-        long_range_past_key_value:Optional[Tuple[torch.Tensor]] = None,
-        faiss_indexes:Tuple=None,
-        mask_by_sim:bool=False,
-        sim_threshold:float=None,
-        topk:int=None,
-        current_layer=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
-        """
-
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value, selected_idx = 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,
-
-            long_range_past_key_value=long_range_past_key_value,
-            faiss_indexes=faiss_indexes,
-            mask_by_sim=mask_by_sim,
-            sim_threshold=sim_threshold,
-            topk=topk,
-            current_layer=current_layer,
-        )
-        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,)
-        
-        if output_attentions:
-            outputs += (selected_idx,)
-
-        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 = ExtendedLlamaConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["LlamaDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-
-    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, ExtendedLlamaModel):
-            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 ExtendedLlamaModel(LlamaPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
-
-    Args:
-        config: LlamaConfig
-    """
-
-    def __init__(self, config: ExtendedLlamaConfig):
-        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([ExtendedLlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-
-        self.mask_by_sim = config.mask_by_sim
-        self.sim_threshold = config.sim_threshold
-        self.topk = config.topk
-        self.use_active_externalism = config.use_active_externalism
-        self.use_active_externalism_by_layer = config.use_active_externalism_by_layer
-        
-        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(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,
-
-        use_active_externalism:Optional[bool]=None,
-        long_range_past_key_values:Optional[List[Tuple[torch.FloatTensor]]] = None,
-        faiss_indexes:Tuple=None,
-        topk:int=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
-        use_active_externalism = (use_active_externalism if use_active_externalism is not None else self.use_active_externalism)
-        topk = (topk if topk is not None else self.topk)
-
-        # 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 = torch.arange( 
-                seq_length_with_past, dtype=torch.long, device=device  #range of position ids is total seq length since we apply rotary pos emb after reading from cache
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length_with_past)
-        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 = () if use_cache else None
-        all_idx = () if output_attentions 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
-
-            long_range_past_key_value = (long_range_past_key_values[idx]
-                                          if (long_range_past_key_values is not None and self.use_active_externalism_by_layer[idx] and use_active_externalism is True) else None)
-
-            if long_range_past_key_value is not None and faiss_indexes is not None:
-                raise NotImplementedError(
-                    'Using faiss and passing key value pairs manually are mutually exclusive right now.')
-
-            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,
-                )
-            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,
-
-                    topk=topk,
-                    long_range_past_key_value=long_range_past_key_value,
-                    faiss_indexes=faiss_indexes,
-                    mask_by_sim=self.mask_by_sim,
-                    sim_threshold=self.sim_threshold,
-                    current_layer=idx,
-                )
-
-            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],)
-
-                all_idx += (layer_outputs[3],) # record which memories were retrieved
-        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, all_idx)
-        )
-
-
-class ExtendedLlamaForCausalLM(LlamaPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config, external_memories=None, **kwargs):
-        super().__init__(config)
-        self.model = ExtendedLlamaModel(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        
-        self.use_active_externalism = config.use_active_externalism
-        self.memory_type = config.memory_type
-        self.memory_device = config.memory_device
-        self._memories = None
-        if external_memories is not None:
-            self._memories = external_memories
-            self.memories = None
-
-        # 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,
-
-        use_active_externalism: Optional[bool]=None,
-        topk: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."
-        ```"""
-
-        if self._memories is not None and self.memories is None: #init memories once on first call 
-            self.memories = self.generate_cache(self._memories, cache_type=self.memory_type)
-
-        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
-
-        use_active_externalism = (use_active_externalism
-            if use_active_externalism is not None else self.use_active_externalism
-        )
-        topk = topk if topk is not None else None
-        
-        long_range_past_key_values = None
-        faiss_indexes = None
-        if hasattr(self, "memories") and isinstance(self.memories, list):
-            long_range_past_key_values = self.memories
-            faiss_indexes = None
-        elif hasattr(self, "memories"):
-            long_range_past_key_values = None
-            faiss_indexes = self.memories
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = 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,
-
-            long_range_past_key_values=long_range_past_key_values,
-            faiss_indexes=faiss_indexes,
-            use_active_externalism=use_active_externalism,
-            topk=topk
-        )
-
-        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 generate_cache(self, 
-                       input_ids:torch.LongTensor, 
-                       stride:int=512,
-                       max_len:int=2048,
-                       cache_type:str='manual'):
-        if cache_type not in ['manual', 'faiss']:
-            raise NotImplementedError(f"Cache type {cache_type} not implemented.")
-        
-        prev_end_loc=0
-        long_range_past_key_values = None
-        faiss_indexes= None
-        for b_idx in range(0, input_ids.size(-1), stride): #generate kv-pairs using stride 
-            end_loc = min(b_idx + max_len, input_ids.size(-1))
-            trg_len = end_loc - prev_end_loc 
-            subseq = input_ids[:, b_idx:end_loc].to(self.model.device)
-            with torch.no_grad():
-                outputs = self.model(subseq, use_cache=True, use_active_externalism=False)
-            to_cache = [(
-                        kv[0][:,:,-trg_len:],
-                        kv[1][:,:,-trg_len:])
-                        for kv in outputs.past_key_values
-                        ]
-            long_range_past_key_values, faiss_indexes = self.cache(to_cache, cache_type, long_range_past_key_values=long_range_past_key_values, faiss_indexes=faiss_indexes)
-            
-            prev_end_loc = end_loc
-            if end_loc == input_ids.size(-1):
-                break
-        if long_range_past_key_values is not None:
-            return long_range_past_key_values
-        else:
-            return faiss_indexes
-
-    def cache(self, 
-              to_cache:List, 
-              cache_type:str='manual', 
-              long_range_past_key_values:List=None, 
-              faiss_indexes:faiss.IndexFlatIP=None, 
-              max_length_cache=100000,
-              verbose=False):
-        if long_range_past_key_values is not None and faiss_indexes is not None:
-            raise NotImplementedError("Using faiss and passing key value pairs manually are mutually exclusive right now.")
-    
-        if cache_type=='faiss': #add one-hot encoding to match layer, head indices
-            one_hot_encodings = F.one_hot(torch.arange(0, self.config.n_heads*self.config.num_hidden_layers))*10
-            if faiss_indexes is None:
-                faiss_indexes = (faiss.IndexFlatIP(to_cache[0][0].size(-1)+one_hot_encodings.size(-1)), faiss.IndexFlatIP(to_cache[0][1].size(-1)*2))
-            kn_index, kv_index = faiss_indexes
-            for b_idx, (k, v) in enumerate(to_cache):
-                k_n = (k/vector_norm(k, ord=2, dim=-1, keepdim=True)).to('cpu')
-                k_n = torch.concat([rearrange(k_n, 'b h s d -> b (h s) d', h=self.config.n_heads), one_hot_encodings[self.config.n_heads*b_idx:self.config.n_heads*(b_idx+1)].unsqueeze(0).repeat_interleave(repeats=k.size(-2), dim=-2)], dim=-1)
-                kn_index.add(k_n.squeeze().numpy())
-
-                k= rearrange(k, 'b h s d -> b (h s) d', h=self.config.n_heads)
-                v= rearrange(v, 'b h s d -> b (h s) d', h=self.config.n_heads)
-                kv_index.add(torch.concat([v.squeeze(), k.squeeze()], dim=1).to('cpu').numpy())
-        else: 
-            if long_range_past_key_values is None:
-                long_range_past_key_values = [(k.to(self.memory_device),v.to(self.memory_device)) for k,v in to_cache]
-            else:
-                long_range_past_key_values = [
-                    (
-                    torch.concat([kv[0], to_cache[ind][0].to(self.memory_device)], dim=2),
-                    torch.concat([kv[1], to_cache[ind][1].to(self.memory_device)], dim=2)
-                    )
-                    for ind, kv in enumerate(long_range_past_key_values)
-                ]
-        if long_range_past_key_values is not None: #set a limit on manual memory length 
-            if long_range_past_key_values[0][0].size(-2) > max_length_cache: 
-                long_range_past_key_values = [
-                    (
-                        kv[0][:, :, -max_length_cache:],
-                        kv[1][:, :, -max_length_cache:]
-                    )
-                    for kv in long_range_past_key_values]
-        if verbose:
-            if cache_type == 'faiss':
-                print(f"{kn_index.ntotal} keys in faiss index")
-            else:
-                print(f"{long_range_past_key_values[0][0].size(-2)} cached kvs")
-            
-        return long_range_past_key_values, (kn_index, kv_index) if cache_type == 'faiss' else None
-    
-    def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
-    ):
-        if 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_(attention_mask == 0, 1)
-            if 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,
-                'use_active_externalism': kwargs.get('use_active_externalism'), #add a few more kwargs for active externalism
-                'topk': kwargs.get('topk', None),
-            }
-        )
-        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