modeling_llama.py

# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch LLaMA model."""
import time
import math
import warnings
from typing import List, Optional, Tuple, Union, Mapping
from contextlib import nullcontext
from dataclasses import dataclass
from collections import defaultdict
from tqdm import tqdm
from accelerate import Accelerator

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache
from transformers.modeling_attn_mask_utils import (
    AttentionMaskConverter,
    _prepare_4d_attention_mask,
    _prepare_4d_causal_attention_mask,
    _prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.utils.import_utils import is_torch_fx_available
from .configuration_llama import LlamaConfig
from .modeling_beacon import Memory

# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
    if not is_torch_greater_or_equal_than_1_13:
        import torch.fx

    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "LlamaConfig"


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
        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)


ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)


class LlamaRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

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

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

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    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.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


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.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


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, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

# Copied from streaming-llm
def apply_rotary_pos_emb_single(x, cos, sin, position_ids):
    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    x_embed = (x * cos) + (rotate_half(x) * sin)
    return x_embed


class LlamaMLP(nn.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]

        if "mlp" in config.beacon_param:            
            self.beacon_up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
            self.beacon_down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
            self.beacon_up_proj._is_hf_initialized = True
            self.beacon_down_proj._is_hf_initialized = True
    
    def _init_beacon_proj(self, beacon_param=None):
        """Initialize the beacon projection weight with that of the ordinal projection."""
        if beacon_param is None:
            beacon_param = self.config.beacon_param
        
        if is_deepspeed_zero3_enabled():
            import deepspeed
            params = [self.up_proj, self.down_proj, self.beacon_up_proj, self.beacon_down_proj]
            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
                if "mlp" in beacon_param:
                    self.beacon_up_proj.weight.data[:] = self.up_proj.weight.data
                    self.beacon_down_proj.weight.data[:] = self.down_proj.weight.data
        else:
            # only copy the value in-place, without tieing the weight
            if "mlp" in beacon_param:
                self.beacon_up_proj.weight.data[:] = self.up_proj.weight.data
                self.beacon_down_proj.weight.data[:] = self.down_proj.weight.data

    def forward(self, x, beacon_size):
        if self.config.pretraining_tp > 1:
            # TODO: support pretraining_tp
            raise NotImplementedError

            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:
            if "mlp" in self.config.beacon_param:
                if beacon_size > 0:
                    ordinal_hidden_states = x[:, :-beacon_size]
                    beacon_hidden_states = x[:, -beacon_size:]

                    # ordinal_up_proj = self.up_proj(ordinal_hidden_states)
                    # beacon_up_proj = self.beacon_up_proj(beacon_hidden_states)
                    # up_proj = torch.cat([ordinal_up_proj, beacon_up_proj], dim=1)
                    # intermediate = self.act_fn(self.gate_proj(x)) * up_proj
                    # ordinal_down_proj = self.down_proj(intermediate[:, :-beacon_size])
                    # beacon_down_proj = self.beacon_down_proj(intermediate[:, -beacon_size:])
                    # down_proj = torch.cat([ordinal_down_proj, beacon_down_proj], dim=1)

                    ordinal_down_proj = self.down_proj(self.act_fn(self.gate_proj(ordinal_hidden_states)) * self.up_proj(ordinal_hidden_states))
                    beacon_down_proj = self.beacon_down_proj(self.act_fn(self.gate_proj(beacon_hidden_states)) * self.beacon_up_proj(beacon_hidden_states))
                    down_proj = torch.cat([ordinal_down_proj, beacon_down_proj], dim=1)
                else:
                    down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
            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 LlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True

        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=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
        self._init_rope()

        # NOTE: add extra parameters for beacon tokens
        # skip post initialization to speed up loading
        if "q" in config.beacon_param:
            self.beacon_q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
            self.beacon_q_proj._is_hf_initialized = True
        if "k" in config.beacon_param:
            self.beacon_k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
            self.beacon_k_proj._is_hf_initialized = True
        if "v" in config.beacon_param:
            self.beacon_v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
            self.beacon_v_proj._is_hf_initialized = True
        if "o" in config.beacon_param:
            self.beacon_o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
            self.beacon_o_proj._is_hf_initialized = True

    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 _init_beacon_proj(self, beacon_param=None):
        """Initialize the beacon projection weight with that of the ordinal projection."""
        if beacon_param is None:
            beacon_param = self.config.beacon_param
        
        if is_deepspeed_zero3_enabled():
            import deepspeed
            params = [self.beacon_q_proj.weight, self.beacon_k_proj.weight, self.beacon_v_proj.weight, self.beacon_o_proj.weight, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight, self.o_proj.weight]
            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
                if "q" in beacon_param:
                    self.beacon_q_proj.weight.data[:] = self.q_proj.weight.data
                if "k" in beacon_param:
                    self.beacon_k_proj.weight.data[:] = self.k_proj.weight.data
                if "v" in beacon_param:
                    self.beacon_v_proj.weight.data[:] = self.v_proj.weight.data
                if "o" in beacon_param:
                    self.beacon_o_proj.weight.data[:] = self.o_proj.weight.data
        else:
            # only copy the value in-place, without tieing the weight
            if "q" in beacon_param:
                self.beacon_q_proj.weight.data[:] = self.q_proj.weight.data
            if "k" in beacon_param:
                self.beacon_k_proj.weight.data[:] = self.k_proj.weight.data
            if "v" in beacon_param:
                self.beacon_v_proj.weight.data[:] = self.v_proj.weight.data
            if "o" in beacon_param:
                self.beacon_o_proj.weight.data[:] = self.o_proj.weight.data

    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 qkv_proj_with_beacon(self, hidden_states, beacon_size=0):
        if beacon_size > 0:
            ordinal_hidden_states = hidden_states[:, :-beacon_size]
            beacon_hidden_states = hidden_states[:, -beacon_size:]
            
            if "q" in self.config.beacon_param:
                ordinal_query_states = self.q_proj(ordinal_hidden_states)
                beacon_query_states = self.beacon_q_proj(beacon_hidden_states)
                query_states = torch.cat([ordinal_query_states, beacon_query_states], dim=1)
            else:
                query_states = self.q_proj(hidden_states)

            if "k" in self.config.beacon_param:
                ordinal_key_states = self.k_proj(ordinal_hidden_states)
                beacon_key_states = self.beacon_k_proj(beacon_hidden_states)
                key_states = torch.cat([ordinal_key_states, beacon_key_states], dim=1)
            else:
                key_states = self.k_proj(hidden_states)
            
            if "v" in self.config.beacon_param:
                ordinal_value_states = self.v_proj(ordinal_hidden_states)
                beacon_value_states = self.beacon_v_proj(beacon_hidden_states)
                value_states = torch.cat([ordinal_value_states, beacon_value_states], dim=1)
            else:
                value_states = self.v_proj(hidden_states)

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

        return query_states, key_states, value_states
    
    def o_proj_with_beacon(self, attn_output, beacon_size=0):
        if beacon_size > 0:
            if "o" in self.config.beacon_param:
                ordinal_attn_output = self.o_proj(attn_output[:, :-beacon_size])
                beacon_attn_output = self.beacon_o_proj(attn_output[:, -beacon_size:])
                attn_output = torch.cat([ordinal_attn_output, beacon_attn_output], dim=1)
            else:
                attn_output = self.o_proj(attn_output)
        else:
            attn_output = self.o_proj(attn_output)
        return attn_output

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

        bsz, q_len, _ = hidden_states.size()
        kv_seq_len = hidden_states.shape[-2]
        past_key, past_value, beacon_size, raw_size_to_cache, window_size = past_key_value
        if past_key is not None:
            past_seq_len = past_key.shape[2]
            kv_seq_len += past_seq_len
        else:
            past_seq_len = 0

        if self.config.pretraining_tp > 1:
            # TODO: support pretraining_tp
            raise NotImplementedError

            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, key_states, value_states = self.qkv_proj_with_beacon(hidden_states, beacon_size)

        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)

        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

        # return keys and values before rope
        # NOTE: incrementally return keys and values for efficiency 
        past_key_value = (key_states, value_states, beacon_size, raw_size_to_cache, window_size)

        if past_key is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key, key_states], dim=2)
            value_states = torch.cat([past_value, value_states], dim=2)

        key_position_ids = position_ids
        # align query position_ids with key
        query_position_ids = key_position_ids[:, -q_len:]

        key_states = apply_rotary_pos_emb_single(key_states, cos, sin, key_position_ids)
        query_states = apply_rotary_pos_emb_single(query_states, cos, sin, query_position_ids)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        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 attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        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:
            # TODO: support pretraining_tp
            raise NotImplementedError
            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_with_beacon(attn_output, beacon_size)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class LlamaSdpaAttention(LlamaAttention):
    """
    Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Adapted from LlamaAttention.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )
        bsz, q_len, _ = hidden_states.size()
        kv_seq_len = hidden_states.shape[-2]
        past_key, past_value, beacon_size, raw_size_to_cache, window_size = past_key_value
        if past_key is not None:
            past_seq_len = past_key.shape[2]
            kv_seq_len += past_seq_len
        else:
            past_seq_len = 0

        query_states, key_states, value_states = self.qkv_proj_with_beacon(hidden_states, beacon_size)

        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)

        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

        # return keys and values before rope
        # NOTE: incrementally return keys and values for efficiency 
        past_key_value = (key_states, value_states, beacon_size, raw_size_to_cache, window_size)

        if past_key is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key, key_states], dim=2)
            value_states = torch.cat([past_value, value_states], dim=2)
        
        key_position_ids = position_ids
        # align query position_ids with key
        query_position_ids = key_position_ids[:, -q_len:]

        key_states = apply_rotary_pos_emb_single(key_states, cos, sin, key_position_ids)
        query_states = apply_rotary_pos_emb_single(query_states, cos, sin, query_position_ids)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        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()}"
                )

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and attention_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=attention_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
            is_causal=self.is_causal and attention_mask is None and q_len > 1,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj_with_beacon(attn_output, beacon_size)

        # for debug
        # if torch.distributed.get_rank() == 4 and self.layer_idx == 0:
        #     torch.save({
        #         "hidden_states": hidden_states,
        #         "past_key_value": past_key_value,
        #         "query_states": query_states,
        #         "key_states": key_states,
        #         "value_states": value_states,
        #         "attn_output": attn_output,
        #         "attention_mask": attention_mask,
        #         "key_position_ids": key_position_ids,
        #     }, "beacon_llama_layer_0")

        return attn_output, None, past_key_value


LLAMA_ATTENTION_CLASSES = {
    "eager": LlamaAttention,
    "sdpa": LlamaSdpaAttention,
}


class LlamaDecoderLayer(nn.Module):
    def __init__(self, config: LlamaConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)

        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,
        use_cache: Optional[bool] = False,
        **kwargs,
    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            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
        """
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

        # NOTE: get beacon_size in case the mlp is included in beacon_param
        past_key, past_value, beacon_size, raw_size_to_cache, window_size = past_key_value

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states, beacon_size)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


LLAMA_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`LlamaConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
    LLAMA_START_DOCSTRING,
)
class LlamaPreTrainedModel(PreTrainedModel):
    config_class = LlamaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LlamaDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = False
    _supports_sdpa = True
    _supports_cache_class = True

    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_()


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 `input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

            Two formats are allowed:
            - a [`~cache_utils.Cache`] instance;
            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
            cache format.

            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


def optional_grad_ctx(with_grad=False):
    if with_grad:
        return nullcontext()
    else:
        return torch.no_grad()

def move_to_device(data, device):
    """
    Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
    """
    if isinstance(data, Mapping):
        return type(data)({k: move_to_device(v, device) for k, v in data.items()})
    elif isinstance(data, (tuple, list)):
        return type(data)(move_to_device(v, device) for v in data)
    elif isinstance(data, torch.Tensor):
        kwargs = {"device": device}
        return data.to(**kwargs)
    else:
        return data

def compute_loss(logits, labels, shift=False):
    """
    Returns:
        token_loss: batch_size, seq_length
    """
    if shift:
        logits = logits[:, :-1, :].contiguous()
        labels = labels[:, 1:].contiguous()

    labels = labels.to(logits.device)
    batch_size = logits.shape[0]

    # NOTE: the loss on -100 labels is 0 by default
    token_loss = torch.nn.functional.cross_entropy(
        logits.flatten(0, 1), 
        labels.reshape(-1), 
        reduction="none"
    ).reshape(batch_size, -1)   # batch_size, seq_len
    
    valid_token_num = (labels != -100).sum(-1)  # batch_size
    all_valid_token_num = valid_token_num.sum()
    
    if all_valid_token_num > 0:
        loss = token_loss.sum() / valid_token_num.sum()
    else:
        loss = token_loss.sum()

    batch_loss = token_loss.sum(-1) / valid_token_num
    # prevent nan
    if (valid_token_num == 0).any():
        batch_loss = batch_loss.masked_fill(valid_token_num == 0, 0.)

    return loss, batch_loss, valid_token_num

@dataclass
class BeaconModelOutput(BaseModelOutputWithPast):
    loss: Optional[torch.FloatTensor] = None
    batch_loss: Optional[torch.FloatTensor] = None
    valid_token_num: Optional[torch.LongTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


@add_start_docstrings(
    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
    LLAMA_START_DOCSTRING,
)
class LlamaModel(LlamaPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]

    Args:
        config: LlamaConfig
    """

    def __init__(self, config: LlamaConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)

        # BEACON: add beacon embedding
        self.beacon_embed_tokens = nn.Embedding(1, config.hidden_size, self.padding_idx)
        self.beacon_embed_tokens._is_hf_initialized = True

        self.layers = nn.ModuleList(
            [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._use_sdpa = config._attn_implementation == "sdpa"
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()
    
    def _init_beacon_embed(self):
        """Initialize the beacon token embedding with that of the eos token."""
        if is_deepspeed_zero3_enabled():
            import deepspeed
            params = [self.beacon_embed_tokens.weight, self.embed_tokens.weight]
            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
                self.beacon_embed_tokens.weight.data[:] = self.embed_tokens.weight.data[self.config.eos_token_id]
        else:
            self.beacon_embed_tokens.weight.data[:] = self.embed_tokens.weight.data[self.config.eos_token_id]

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        # BEACON: always use cache
        use_cache = True

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape[:2]
        elif inputs_embeds is not None:
            batch_size, seq_length = inputs_embeds.shape[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        # BEACON: create position_ids for all keys including past_keys
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        seq_length_with_past = seq_length
        past_key_values_length = 0
        past_key, past_value, beacon_size, raw_size_to_cache, window_size = past_key_values[0]
        if past_key is not None:
            past_key_values_length = past_key.shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        # BEACON: separately embed ordinal tokens and beacon tokens because ordinal tokens do not receive gradients
        if beacon_size > 0:
            ordinal_input_ids = input_ids[:, :-beacon_size]
            beacon_input_ids = input_ids[:, -beacon_size:]
            ordinal_inputs_embeds = self.embed_tokens(ordinal_input_ids)
            # bias beacon_token_ids because they are newly initialized
            beacon_input_embeds = self.beacon_embed_tokens(beacon_input_ids - self.config.vocab_size)
            inputs_embeds = torch.cat([ordinal_inputs_embeds, beacon_input_embeds], dim=1)
        else:
            inputs_embeds = self.embed_tokens(input_ids)

        # BEACON: by default to use sdpa because flash attention do not support customized attention_mask
        # when beacon_size > 0, we need to modify attention mask
        if self._use_sdpa and not output_attentions and beacon_size <= 0:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
            )
        
        position_ids = torch.arange(seq_length_with_past, dtype=torch.long, device=device).repeat(batch_size, 1)

        if beacon_size > 0:
            # number of tokens to condense by the beacons
            condensing_size = window_size - raw_size_to_cache
            # number of tokens to be compressed by one beacon
            token_per_beacon = condensing_size // beacon_size
            # number of tokens in current window (containing cached raw activations)
            window_size_with_beacon = window_size + beacon_size
            # number of beacon activations in cache
            memory_size = seq_length_with_past - window_size_with_beacon

            min_value = torch.finfo(inputs_embeds.dtype).min

            if self.config.beacon_attn == "segmentation":
                # each beacon can attend to its corresponding sub-interval
                # beacon_size, token_per_beacon
                indices = torch.arange(token_per_beacon * beacon_size, device=device).view(beacon_size, -1)
                # beacon_size, window_size
                ordinal_attention_mask = torch.full_like(attention_mask[0, 0, -beacon_size:, -window_size_with_beacon: -beacon_size], min_value)
                ordinal_attention_mask.scatter_(dim=-1, index=indices, value=0)

                beacon_attention_mask = torch.full_like(attention_mask[0, 0, -beacon_size:, -beacon_size:], min_value).fill_diagonal_(0)

                attention_mask[..., -beacon_size:, -window_size_with_beacon: -beacon_size] = ordinal_attention_mask
                attention_mask[..., -beacon_size:, -beacon_size:] = beacon_attention_mask
                if not self.config.beacon_attend_previous:
                    # prevent attending to beacons of previous windows
                    attention_mask[..., -beacon_size:, :-window_size_with_beacon] = min_value

                # NOTE: we must modify the position_ids here instead of inside the self_attn forward, otherwise the version of position_ids is incompatible when enabling gradient checkpointing
                # the beacon token is next to the last oridinal token it attends to
                beacon_position_ids = position_ids.new_full((beacon_size,), fill_value=token_per_beacon + memory_size)
                position_ids[:, -beacon_size:] = beacon_position_ids

            elif self.config.beacon_attn == "step-expansion":
                # each beacon can attend to one more sub-interval than its predecessor
                # beacon_size, 1
                beacon_arange = torch.arange(beacon_size, device=device)[:, None]
                beacon_arange = (beacon_arange + 1) * token_per_beacon
                # beacon_size, window_size
                ordinal_arange = torch.arange(window_size, device=device).expand(beacon_size, -1)
                # batch_size, window_size
                ordinal_attention_mask = torch.full_like(attention_mask[..., -beacon_size:, -window_size_with_beacon: -beacon_size], min_value)                

                ordinal_attention_mask = ordinal_attention_mask.masked_fill(ordinal_arange < beacon_arange, 0.)
                beacon_attention_mask = torch.full_like(attention_mask[0, 0, -beacon_size:, -beacon_size:], min_value).fill_diagonal_(0)

                attention_mask[..., -beacon_size:, -window_size_with_beacon: -beacon_size] = ordinal_attention_mask
                attention_mask[..., -beacon_size:, -beacon_size:] = beacon_attention_mask
                if not self.config.beacon_attend_previous:
                    # prevent attending to beacons of previous windows
                    attention_mask[..., -beacon_size:, :-window_size_with_beacon] = min_value

                # NOTE: we must modify the position_ids here instead of inside the self_attn forward, otherwise the version of position_ids is incompatible when enabling gradient checkpointing
                # the beacon token is next to the last oridinal token it attends to
                beacon_position_ids = torch.arange(token_per_beacon, token_per_beacon * beacon_size + 1, token_per_beacon) + memory_size
                position_ids[:, -beacon_size:] = beacon_position_ids
            
            elif self.config.beacon_attn == "full-coverage":
                # each beacon can attend to all ordinal tokens
                # beacon_size, window_size
                ordinal_attention_mask = torch.full_like(attention_mask[0, 0, -beacon_size:, -window_size_with_beacon: -beacon_size], 0)
                beacon_attention_mask = torch.full_like(attention_mask[0, 0, -beacon_size:, -beacon_size:], min_value).fill_diagonal_(0)
                attention_mask[..., -beacon_size:, -window_size_with_beacon: -beacon_size] = ordinal_attention_mask
                attention_mask[..., -beacon_size:, -beacon_size:] = beacon_attention_mask
                if not self.config.beacon_attend_previous:
                    # prevent attending to beacons of previous windows
                    attention_mask[..., -beacon_size:, :-window_size_with_beacon] = min_value
                # the beacon token is next to the last oridinal token it attends to
                beacon_position_ids = position_ids[0, -beacon_size]
                position_ids[:, -beacon_size:] = beacon_position_ids
            
            else:
                raise NotImplementedError

        # print(f"beacon_size:        {beacon_size}")
        # print(f"raw_size_to_cache:  {beacon_size}")
        # print(f"position_ids:       {position_ids}")
        # print(f"attention_mask:\n{attention_mask}")
        # x = input()
        # if x == "s":
        #     return

        # embed positions
        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        # BEACON: still use tuple to organize cache
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            # BEACON: slice out the past_key_value of the corresponding layer
            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_value,
                    output_attentions,
                    use_cache,
                )
            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,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # for debug
        # if torch.distributed.get_rank() == 4:
        #     torch.save({
        #         "hidden_states": hidden_states,
        #         "past_key_values": past_key_values,
        #         "attention_mask": attention_mask,
        #         "position_ids": position_ids,
        #     }, "beacon_llama_inputs")

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class LlamaForCausalLM(LlamaPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = LlamaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    def set_memory(self):
        config: LlamaConfig = self.config
        self.memory = Memory(
            model_config=config,
            beacon_window=config.beacon_window,
            beacon_stride=config.beacon_stride,
            beacon_attn=config.beacon_attn,
            beacon_attend_previous=config.beacon_attend_previous,
            beacon_ratio=config.beacon_ratio,
            beacon_stride_mix=config.beacon_stride_mix,
            beacon_ratio_mix=config.beacon_ratio_mix,
            beacon_param=config.beacon_param,
            k_seq_dim=2,
            v_seq_dim=2,
            retrieval_method=config.retrieval_method,
            retrieval_topk=config.retrieval_topk,
        )

    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

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        """Override the default from_pretrained to extend vocab size according to beacon_size."""
        model, loading_info = super().from_pretrained(*args, **kwargs, output_loading_info=True)
        missing_keys = loading_info["missing_keys"]
        # only initialize beacon weights when they are missing from the checkpoint
        beacon_param = set()
        for missing_key in missing_keys:
            if "beacon_embed_tokens" in missing_key:
                model.model._init_beacon_embed()    
            elif "beacon_q_proj" in missing_key:
                beacon_param.add("q")
            elif "beacon_k_proj" in missing_key:
                beacon_param.add("k")
            elif "beacon_v_proj" in missing_key:
                beacon_param.add("v")
            elif "beacon_o_proj" in missing_key:
                beacon_param.add("o")
            elif "beacon_up_proj" in missing_key:
                beacon_param.add("mlp")        

        # initialize weights of possible q,k,v,o,mlp
        for layer in model.model.layers:
            layer.self_attn._init_beacon_proj(beacon_param)
            layer.mlp._init_beacon_proj(beacon_param)
        return model

    def _native_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,
        shift_labels: Optional[bool] = True,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BeaconModelOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        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,
        )

        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
        batch_loss = None
        valid_token_num = None
        
        if labels is not None:
            loss, batch_loss, valid_token_num = compute_loss(logits, labels, shift=shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return BeaconModelOutput(
            loss=loss,
            batch_loss=batch_loss,
            valid_token_num=valid_token_num,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def _beacon_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,
    ):
        # t1 = time.time()
        # initialize cache
        self.memory.prepare(
            input_ids=input_ids, 
            attention_mask=attention_mask, 
            labels=labels
        )
        # t2 = time.time()

        # after the first window, one token at a time
        while not self.memory.finish:
            # t3 = time.time()

            input_ids, attention_mask, past_key_values, labels = self.memory.step()

            # t4 = time.time()

            outputs = self._native_forward(
                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,
                labels=labels,
                # NOTE: the labels have been shifted so that all tokens in the window have the proper loss
                shift_labels=False,
            )
            # t5 = time.time()

            # update past_key_values
            self.memory.update_memory(outputs.past_key_values)

            # t6 = time.time()

            if labels is not None:
                # if torch.distributed.get_rank() == 0:
                #     print(outputs.batch_loss, outputs.valid_token_num)
                # update loss and past_key_values
                self.memory.update_loss(outputs.batch_loss, outputs.valid_token_num)

            # t7 = time.time()

            # print(f"Loop step time:         {t4-t3}")
            # print(f"Loop forward time:      {t5-t4}")
            # print(f"Loop update time:       {t6-t5}")
            # print(f"Loop loss time:         {t7-t6}")
            # input()

        # t8 = time.time()

        # output loss, past_key_values, and perplexity
        outputs = self.memory.output(outputs)

        # t9 = time.time()
        # print(f"Prepare time:           {t2-t1}")
        # print(f"Output time:            {t9-t8}")
        return outputs
    
    def forward(self, **kwargs):
        """Forward computation over a batch of sequences.
        """
        # only allow gradient when training
        with optional_grad_ctx(with_grad=self.training):
            return self._beacon_forward(**kwargs)

    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,
            }
        )
        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



@torch.no_grad()
def evaluate_perplexity(model, dataloader, accelerator:Optional[Accelerator]=None):
    if accelerator is not None and type(dataloader) == torch.utils.data.DataLoader:
        # if the dataloader has been prepared, we shall not prepare it twice, especially in case of deepspeed
        dataloader = accelerator.prepare(dataloader)

    all_loss = defaultdict(list)
    for i, x in enumerate(tqdm(dataloader, desc="Computing Perplexity")):
        # NOTE: important to reset memory for every batch
        if hasattr(model, "memory") and model.memory is not None:
            model.memory.reset()

        # the seq id
        index = x.pop("index")
        # length is used to group training data, no use here
        length = x.pop("length", None)

        output = model(**x)

        # NOTE: we need the loss for each element in the batch for accurate computation, because the number of valid tokens may differ among elements
        if hasattr(output, "batch_loss"):
            # output from our model has batch_loss by default
            batch_loss = output.batch_loss
            valid_token_num = output.valid_token_num
        else:
            # output from other models does not
            loss, batch_loss, valid_token_num = compute_loss(output.logits, x["labels"], shift=True)

        if accelerator is not None:
            # num_device * batch_size
            index = accelerator.gather_for_metrics(index)
            batch_loss = accelerator.gather_for_metrics(batch_loss)
            valid_token_num = accelerator.gather_for_metrics(valid_token_num)

        for _id, _loss, _num in zip(index.tolist(), batch_loss.tolist(), valid_token_num.tolist()):
            # loss times num is the total loss of all valid tokens
            all_loss[_id].append((_loss * _num, _num))

    for _id, loss_and_num in all_loss.items():
        # sum up the loss for all valid tokens in the entire sequence, and divide the number of valid tokens
        all_loss[_id] = sum([x[0] for x in loss_and_num]) / sum(x[1] for x in loss_and_num)
    
    # average across then take exp
    perplexity = math.exp(sum(all_loss.values()) / len(all_loss))
    return perplexity


@torch.no_grad()
def evaluate_generation(model, dataloader, accelerator:Optional[Accelerator]=None, tokenizer=None, return_new_tokens_only=True, return_decoded=True, **generation_config):
    if accelerator is not None and type(dataloader) == torch.utils.data.DataLoader:
        # if the dataloader has been prepared, we shall not prepare it twice, especially in case of deepspeed
        dataloader = accelerator.prepare(dataloader)

    all_indices = []
    all_outputs = []
    for i, x in enumerate(tqdm(dataloader, desc="Computing Generation")):
        # NOTE: important to reset memory for every batch
        if hasattr(model, "memory") and model.memory is not None:
            model.memory.reset()

        indices = x.pop("index")
        outputs = model.generate(**x, **generation_config)
        if return_new_tokens_only:
            start_idx = x["input_ids"].shape[1]
            outputs = outputs[:, start_idx:]

        if accelerator is not None:
            # must be contiguous
            outputs = outputs.contiguous()
            outputs = accelerator.pad_across_processes(outputs, pad_index=tokenizer.pad_token_id, dim=1)
            outputs = accelerator.gather_for_metrics(outputs)
            indices = accelerator.gather_for_metrics(indices)

        outputs = outputs.tolist()
        indices = indices.tolist()
        if return_decoded:
            outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
        all_indices.extend(indices)
        all_outputs.extend(outputs)

    return all_indices, all_outputs