modeling.py

# 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.

# This code has been adapted from Meta and Huggingface and inherits the above lisence.
# The original code can be found here:
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
# We annotate the edited code below with 'EM' comments to indicate where we have made changes.
"""PyTorch Extended LLaMA model."""
import math
from typing import List, Optional, Tuple, Union

import faiss
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
from torch import nn
from torch.linalg import vector_norm
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)
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 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.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat(
            [
                torch.zeros(
                    tgt_len, past_key_values_length, dtype=dtype, device=device
                ),
                mask,
            ],
            dim=-1,
        )
    return mask[None, None, :, :].expand(
        bsz, 1, tgt_len, tgt_len + past_key_values_length
    )


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(
        inverted_mask.to(torch.bool), torch.finfo(dtype).min
    )


class LlamaRMSNorm(nn.Module):
    """LlamaRMSNorm is equivalent to T5LayerNorm"""

    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):
        """Apply RMS Norm"""
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


class LlamaRotaryEmbedding(torch.nn.Module):
    """Rotary Positional Embedding"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (
            self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
        )
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings,
            device=self.inv_freq.device,
            dtype=torch.get_default_dtype(),
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(
            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
        )

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer(
            "cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
        )
        self.register_buffer(
            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
        )

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        return (
            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
        )


class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    def __init__(
        self,
        dim,
        max_position_embeddings=2048,
        base=10000,
        device=None,
        scaling_factor=1.0,
    ):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(
            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
        )
        t = t / self.scaling_factor

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer(
            "cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
        )
        self.register_buffer(
            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
        )


class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

    def __init__(
        self,
        dim,
        max_position_embeddings=2048,
        base=10000,
        device=None,
        scaling_factor=1.0,
    ):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        if seq_len > self.max_position_embeddings:
            base = self.base * (
                (self.scaling_factor * seq_len / self.max_position_embeddings)
                - (self.scaling_factor - 1)
            ) ** (self.dim / (self.dim - 2))
            inv_freq = 1.0 / (
                base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(
            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
        )

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer(
            "cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
        )
        self.register_buffer(
            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
        )


def 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):
    """Apply rotary positional embedding to q and k."""
    # 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
    )  
    # EM: 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):
    """MLP Module"""

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        if self.config.pretraining_tp > 1:
            slice = self.intermediate_size // self.config.pretraining_tp
            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
            down_proj_slices = self.down_proj.weight.split(slice, dim=1)

            gate_proj = torch.cat(
                [
                    F.linear(x, gate_proj_slices[i])
                    for i in range(self.config.pretraining_tp)
                ],
                dim=-1,
            )
            up_proj = torch.cat(
                [
                    F.linear(x, up_proj_slices[i])
                    for i in range(self.config.pretraining_tp)
                ],
                dim=-1,
            )

            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
            down_proj = [
                F.linear(intermediate_states[i], down_proj_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            down_proj = sum(down_proj)
        else:
            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

        return down_proj


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(
        batch, num_key_value_heads, n_rep, slen, head_dim
    )
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


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.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta

        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_key_value_heads * self.head_dim, bias=False
        )
        self.v_proj = nn.Linear(
            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
        )
        self.o_proj = nn.Linear(
            self.num_heads * self.head_dim, self.hidden_size, bias=False
        )
        self._init_rope()

    def _init_rope(self):
        if self.config.rope_scaling is None:
            self.rotary_emb = LlamaRotaryEmbedding(
                self.head_dim,
                max_position_embeddings=self.max_position_embeddings,
                base=self.rope_theta,
            )
        else:
            scaling_type = self.config.rope_scaling["type"]
            scaling_factor = self.config.rope_scaling["factor"]
            if scaling_type == "linear":
                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    scaling_factor=scaling_factor,
                    base=self.rope_theta,
                )
            elif scaling_type == "dynamic":
                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    scaling_factor=scaling_factor,
                    base=self.rope_theta,
                )
            else:
                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

    def _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,
        output_retrieved_memory_idx: 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]]]:
        """forward"""
        bsz, q_len, _ = hidden_states.size()

        if self.config.pretraining_tp > 1:
            key_value_slicing = (
                self.num_key_value_heads * self.head_dim
            ) // self.config.pretraining_tp
            query_slices = self.q_proj.weight.split(
                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
            )
            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

            query_states = [
                F.linear(hidden_states, query_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            query_states = torch.cat(query_states, dim=-1)

            key_states = [
                F.linear(hidden_states, key_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            key_states = torch.cat(key_states, dim=-1)

            value_states = [
                F.linear(hidden_states, value_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            value_states = torch.cat(value_states, dim=-1)

        else:
            query_states = self.q_proj(hidden_states)
            key_states = self.k_proj(hidden_states)
            value_states = self.v_proj(hidden_states)

        query_states = query_states.view(
            bsz, q_len, self.num_heads, self.head_dim
        ).transpose(1, 2)
        key_states = key_states.view(
            bsz, q_len, self.num_key_value_heads, self.head_dim
        ).transpose(1, 2)
        value_states = value_states.view(
            bsz, q_len, self.num_key_value_heads, self.head_dim
        ).transpose(1, 2)

        # EM: Read from cache before position information is added
        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, key_states = apply_rotary_pos_emb(
            query_states, key_states, cos, sin, position_ids
        )

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        bsz, nh, s_q, hd = query_states.shape

        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()}"
            )

        # EM: Retrieve memories from cache or faiss indexes
        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
                k_cache = repeat_kv(k_cache, self.num_key_value_groups)
                v_cache = repeat_kv(v_cache, self.num_key_value_groups)

                s_cache = k_cache.size(-2)

                k_cache = k_cache.to(key_states.device)
                v_cache = v_cache.to(key_states.device)

                # Normalize query and key vectors
                q_n = query_states / vector_norm(
                    query_states, 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) # Retrieve topk memories

                reshaped_idx = idx.reshape(bsz, nh, s_q * topk)

                selected_k = k_cache.gather(
                    dim=-2, index=reshaped_idx.unsqueeze(-1).expand(-1, -1, -1, hd)
                )
                selected_v = v_cache.gather(
                    dim=-2, index=reshaped_idx.unsqueeze(-1).expand(-1, -1, -1, hd)
                )

            elif faiss_indexes is not None:  # FAISS indexes
                kn_index, kv_index = faiss_indexes
                q_n = query_states / vector_norm(
                    query_states, ord=2, dim=-1, keepdim=True
                )

                # One-hot encoding for layer, head to only retrieve memories from the same layer, head
                one_hot_encodings = (
                    F.one_hot(
                        torch.arange(
                            0,
                            nh * self.config.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()

                if kn_index.ntotal / (nh * self.config.num_hidden_layers) < topk:
                    topk = kn_index.ntotal / (nh * self.config.num_hidden_layers)

                val, idx = kn_index.search(q_n.to("cpu").detach().numpy(), k=topk)
                val = torch.tensor(val - 100).reshape(bsz, nh, s_q, topk) #Similarity includes scale factor from one-hot encoding
                reshaped_idx = torch.tensor(
                    idx % (kn_index.ntotal / (nh * self.config.num_hidden_layers))
                ).reshape(bsz, nh, s_q * topk)

                selected_k = rearrange(
                    torch.tensor(kv_index.reconstruct_batch(idx.flatten()))[:, :hd],
                    "(h s) d -> 1 h s d",
                    h=nh,
                ).to(query_states.device)

                selected_v = rearrange(
                    torch.tensor(kv_index.reconstruct_batch(idx.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)
            # EM: Mask by similarity
            if mask_by_sim:
                sim_mask = (
                    rearrange(~(val > sim_threshold).bool(), "b h s i -> b h (s i)")
                    .unsqueeze(-2)
                    .expand(-1, -1, s_q, -1)
                ).to(query_states.device)
                attn_weight_cache = attn_weight_cache.masked_fill(
                    sim_mask, torch.finfo(query_states.dtype).min
                )
            # EM: Concatenate cache and current attention weights, values
            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
        
        # EM: Create mask for external memories, queries only attend to their own memories
        def _create_external_memories_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()}"
                )
            # EM: Concatenate attention mask with external memories mask
            if long_range_past_key_value is not None or faiss_indexes is not None:
                memory_mask = _create_external_memories_mask(
                    k=topk, s_q=s_q, device=attn_weights.device
                )
                attention_mask = (
                    torch.cat(
                        [
                            memory_mask,
                            attention_mask.squeeze(dim=[0, 1]),
                        ],
                        dim=1,
                    )
                    .unsqueeze(dim=0)
                    .unsqueeze(dim=1)
                )
            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(
            attn_weights, dim=-1, dtype=torch.float32
        ).to(query_states.dtype)
        attn_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).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        if self.config.pretraining_tp > 1:
            attn_output = attn_output.split(
                self.hidden_size // self.config.pretraining_tp, dim=2
            )
            o_proj_slices = self.o_proj.weight.split(
                self.hidden_size // self.config.pretraining_tp, dim=1
            )
            attn_output = sum(
                F.linear(attn_output[i], o_proj_slices[i])
                for i in range(self.config.pretraining_tp)
            )
        else:
            attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        if not output_retrieved_memory_idx or (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):
    """Decoder Layer for LLaMA"""

    def __init__(self, config: ExtendedLlamaConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = ExtendedLlamaAttention(config=config)
        self.mlp = LlamaMLP(config)
        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,
        output_retrieved_memory_idx: 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,
            output_retrieved_memory_idx=output_retrieved_memory_idx,
            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_retrieved_memory_idx:
            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 ([`ExtendedLlamaConfig`]):
            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):
    """Wrapper class"""

    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_external_mind = config.use_external_mind
        self.use_external_mind_by_layer = config.use_external_mind_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_retrieved_memory_idx: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        use_external_mind: Optional[bool] = None,
        long_range_past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
        faiss_indexes: Tuple = None,
        topk: int = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """forward"""
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_retrieved_memory_idx = (
            output_retrieved_memory_idx
            if output_retrieved_memory_idx is not None
            else False
        )
        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_external_mind = (
            use_external_mind
            if use_external_mind is not None
            else self.use_external_mind
        )
        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

        # EM: Range of position ids is total seq length since we apply rotary pos emb after reading from cache
        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,
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length_with_past)
        else:
            position_ids = position_ids.view(-1, seq_length_with_past).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_retrieved_memory_idx 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_external_mind_by_layer[idx]
                    and use_external_mind 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, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                )
            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,
                    output_retrieved_memory_idx=output_retrieved_memory_idx,
                    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],)

            if output_retrieved_memory_idx:
                idx = (
                    3
                    if (use_cache & output_attentions)
                    else 2
                    if (use_cache or output_attentions)
                    else 1
                )
                all_idx += (layer_outputs[idx],)  # 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,
                    all_idx,
                ]
                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),  # EM: Return idx of retrieved memories
        )


class ExtendedLlamaForCausalLM(LlamaPreTrainedModel):
    """LlamaForCausalLM"""

    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config, external_memories:list=None):
        super().__init__(config)
        self.model = ExtendedLlamaModel(config)
        self.vocab_size = config.vocab_size
        self.tokenizer_all_special_ids = config.tokenizer_all_special_ids
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.use_external_mind = config.use_external_mind
        self.memory_type = config.memory_type
        self.memory_device = config.memory_device
        self.remove_special_ids = config.remove_special_ids
        self.memory_ids = None
        self.memories = None

        # EM: Memory token ids
        if external_memories is not None:
            self.memory_ids = external_memories

        # Initialize weights and apply final processing
        self.post_init()

    # EM: Clear memory cache
    def clear_memory(self):
        """Clear memory cache."""
        self.memory_ids = None
        self.memories = None

    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):
        """Set output embeddings."""
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        """Set decoder."""
        self.model = decoder

    def get_decoder(self):
        """Get decoder."""
        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,
        output_retrieved_memory_idx: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        use_external_mind: 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."
        ```"""

        # EM: Generate key value cache once on first call
        if (
            self.memory_ids is not None and self.memories is None
        ): 
            self.memory_ids = torch.tensor([self.memory_ids], device=self.device) if type(self.memory_ids)==list else self.memory_ids
            self.memories = self.generate_cache(
                self.memory_ids, cache_type=self.memory_type,
            )
            # EM: Remove special tokens from memory cache
            if self.remove_special_ids:
                idx_to_remove = [
                    token_idx
                    for token_idx, token in enumerate(self.memory_ids[0])
                    if token in self.tokenizer_all_special_ids
                ]
                if self.memory_type == "manual":
                    mask = torch.ones(self.memories[0][0].size(), dtype=torch.bool)
                    mask[:, :, idx_to_remove, :] = False

                    new_size = (
                        self.memories[0][0].size(0),
                        self.memories[0][0].size(1),
                        -1,
                        self.memories[0][0].size(3),
                    )
                    self.memories = [
                        (ks[mask].view(new_size), vs[mask].view(new_size))
                        for ks, vs in self.memories
                    ]
                else:
                    kn_index, kv_index = self.memories
                    all_idx_to_remove = [
                        [
                            i
                            for i in range(0, kn_index.ntotal)
                            if (
                                i
                                % (
                                    kn_index.ntotal
                                    / (
                                        self.config.num_attention_heads
                                        * self.config.num_hidden_layers
                                    )
                                )
                            )
                            == j
                        ]
                        for j in idx_to_remove
                    ]
                    kn_index.remove_ids(
                        np.array(all_idx_to_remove).flatten().astype("int64")
                    )
                    kv_index.remove_ids(
                        np.array(all_idx_to_remove).flatten().astype("int64")
                    )

        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_retrieved_memory_idx = (
            output_retrieved_memory_idx
            if output_retrieved_memory_idx is not None
            else False
        )
        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_external_mind = (
            use_external_mind
            if use_external_mind is not None
            else self.use_external_mind
        )
        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
        elif hasattr(self, "memories"):
            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_retrieved_memory_idx=output_retrieved_memory_idx,
            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_external_mind=use_external_mind,
            topk=topk,
        )

        hidden_states = outputs[0]
        if self.config.pretraining_tp > 1:
            lm_head_slices = self.lm_head.weight.split(
                self.vocab_size // self.config.pretraining_tp, dim=0
            )
            logits = [
                F.linear(hidden_states, lm_head_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            logits = torch.cat(logits, dim=-1)
        else:
            logits = self.lm_head(hidden_states)
        logits = logits.float()

        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,
        )

    # EM: Add method to generate key-value cache
    def generate_cache(
        self,
        input_ids: torch.LongTensor,
        stride: int = 512,
        max_len: int = 3072,
        cache_type: str = "manual",
    ):
        """Stride over memory inputs to get kv pairs"""
        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.inference_mode():
                outputs = self.model(
                    subseq,
                    use_cache=True,
                    use_external_mind=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

    # EM: Add method to cache key value pairs
    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,
    ):
        """Cache key value pairs for Extended Mind attention."""
        if (long_range_past_key_values is not None) & (faiss_indexes is not None):
            raise NotImplementedError(
                "Using faiss and passing key value pairs manually are mutually exclusive right now."
            )
        # To avoid spinning up a new index for each layer, we add one-hot encodings to the keys so that queries match with the appropriate layer, head
        if cache_type == "faiss":  # add one-hot encoding to match layer, head indices
            one_hot_encodings = (
                F.one_hot(
                    torch.arange(
                        0,
                        self.config.num_attention_heads * self.config.num_hidden_layers,
                    )
                )
                * 10
            )
            # New indices, one to store normalized keys with one-hot encodings, another to retrieve kv pairs without normalization
            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][0].size(-1) * 2),
                )
            kn_index, kv_index = faiss_indexes
            for l_idx, (k, v) in enumerate(to_cache):
                k_n = (k / vector_norm(k, ord=2, dim=-1, keepdim=True)).to("cpu") #Normalize keys for cosine sim
                # Indices are 2 dimensional, so flatten 

                # Add normalized keys with one-hot encodings
                k_n = torch.concat(
                    [
                        rearrange(
                            k_n,
                            "b h s d -> b (h s) d",
                            h=self.config.num_attention_heads,
                        ),
                        one_hot_encodings[
                            self.config.num_attention_heads
                            * l_idx : self.config.num_attention_heads
                            * (l_idx + 1)
                        ]
                        .unsqueeze(0)
                        .repeat_interleave(repeats=k.size(-2), dim=-2),
                    ],
                    dim=-1,
                )
                kn_index.add(k_n.squeeze().numpy())

                # Add unnormalized keys and values
                k = rearrange(
                    k, "b h s d -> b (h s) d", h=self.config.num_attention_heads
                )
                v = rearrange(
                    v, "b h s d -> b (h s) d", h=self.config.num_attention_heads
                )
                kv_index.add(
                    torch.concat([k.squeeze(), v.squeeze()], dim=1).to("cpu").numpy()
                )
        else:
            # Simply use list to store key value pairs
            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 `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_external_mind": kwargs.get("use_external_mind"), # EM: Add config here
                "topk": kwargs.get("topk"),
                "output_retrieved_memory_idx": kwargs.get("output_retrieved_memory_idx"),
            }
        )
        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