long_llama_3b_instruct / modeling_longllama.py

Szymon Tworkowski

init release

4552b82 11 months ago

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	# coding=utf-8
	# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
	# and OPT implementations in this library. It has been modified from its
	# original forms to accommodate minor architectural differences compared
	# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch LongLLaMA model."""
	from dataclasses import dataclass
	import math
	from typing import List, Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	SequenceClassifierOutputWithPast,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_longllama import LongLlamaConfig
	from .longllama_utils import mem_apply_update, LongLlamaMemCache, LongLlamaMemConfig


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "LongLlamaConfig"


	@dataclass
	class LongLlamaModelOutputWithPast(BaseModelOutputWithPast):
	"""
	Based on BaseModelOutputWithPast

	Args:
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	mem_caches (`tuple(LongLlamaMemCache))`, optional, returned for layers with memory cache enabled):
	For the layers without memory None is returned
	"""

	mem_caches: Optional[LongLlamaMemCache] = None


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	def _make_causal_mask(
	input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
	):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
	mask_cond = torch.arange(mask.size(-1), device=device)
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

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


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

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

	inverted_mask = 1.0 - expanded_mask

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


	# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->LongLlama
	class LongLlamaRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	LongLlamaRMSNorm is equivalent to T5LayerNorm
	"""
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

	def forward(self, hidden_states):
	input_dtype = hidden_states.dtype
	variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

	return (self.weight * hidden_states).to(input_dtype)


	# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->LongLlama
	class LongLlamaRotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	super().__init__()
	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
	self.register_buffer("inv_freq", inv_freq)

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

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
	if seq_len > self.max_seq_len_cached:
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
	self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(x.dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
	return (
	self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
	self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
	)


	def rotate_half(x):
	"""Rotates half the hidden dims of the input."""
	x1 = x[..., : x.shape[-1] // 2]
	x2 = x[..., x.shape[-1] // 2 :]
	return torch.cat((-x2, x1), dim=-1)


	# Based on transformers.models.llama.modeling_llama.apply_rotary_pos_emb
	def rotate_one(x, cos, sin, position_ids):
	if len(position_ids.shape) != 2 or x.shape[0] != position_ids.shape[0] or x.shape[-2] != position_ids.shape[1]:
	raise ValueError(f"Position ids shoud have shape [bsz, seq_len] got {position_ids.shape}")
	# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
	cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
	sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
	cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	x_embed = (x * cos) + (rotate_half(x) * sin)
	return x_embed


	def rotate_as_if_first(x, rotary_emb):
	# x: [bs, num_attention_heads, seq_len, head_size]
	# apply rotary as if all elements were first in the sequence
	cos, sin = rotary_emb(x, x.shape[-2])
	return rotate_one(x, cos, sin, torch.zeros(x.shape[0], x.shape[-2], dtype=torch.long, device=cos.device))


	# Copied from transformers.models.llama.modeling_llama.LlamaMLP with Llama->LongLlama
	class LongLlamaMLP(nn.Module):
	def __init__(
	self,
	hidden_size: int,
	intermediate_size: int,
	hidden_act: str,
	):
	super().__init__()
	self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
	self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
	self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
	self.act_fn = ACT2FN[hidden_act]

	def forward(self, x):
	return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


	# Modified transformers.models.llama.modeling_llama.LlamaAttention
	class LongLlamaAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper with FoT modifications"""

	def __init__(self, config: LongLlamaConfig, mem_config: Optional[LongLlamaMemConfig] = None):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.max_position_embeddings = config.max_position_embeddings
	self.max_cache = self.max_position_embeddings

	if (self.head_dim * self.num_heads) != self.hidden_size:
	raise ValueError(
	f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
	f" and `num_heads`: {self.num_heads})."
	)
	self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
	self.rotary_emb = LongLlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
	self.mem_config = mem_config

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	mem_cache: Optional[LongLlamaMemCache] = None,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	if attention_mask is None:
	tgt_seq_len = hidden_states.shape[-2]
	if past_key_value is not None:
	src_seq_len = past_key_value[0].shape[-2] + tgt_seq_len
	else:
	src_seq_len = tgt_seq_len

	attention_mask = torch.zeros(
	hidden_states.shape[0],
	1,
	tgt_seq_len,
	src_seq_len,
	device=hidden_states.device,
	dtype=hidden_states.dtype,
	)
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	position_ids = position_ids[:, None, :, None]

	if position_ids.shape != (key_states.shape[0], 1, key_states.shape[-2], 1):
	raise ValueError("position_ids should match batch and seq_len of the input")

	mem_no_local_cache = self.mem_config is not None and past_key_value is None and (not use_cache)
	mem_and_local_cache = self.mem_config is not None and use_cache
	# positonal embeddings can be disabled for memory layers
	use_positionals = self.mem_config is None or self.mem_config.positionals

	if mem_no_local_cache:
	# the whole context window will be moved to memory cache after the attention
	if use_positionals:
	# positionally embedd memory content as first token in the sequence
	rfst_key_states = rotate_as_if_first(key_states, self.rotary_emb)
	else:
	rfst_key_states = key_states
	# attention_mask [bsz, 1, tgt_seq_len, src_seq_len]
	# we base the mask on the last token in the context window
	mem_update = LongLlamaMemCache(
	keys=rfst_key_states.to(self.mem_config.cache_dtype),
	values=value_states.to(self.mem_config.cache_dtype),
	masks=attention_mask[..., -1, :, None],
	)

	if past_key_value is not None:
	past_local_cache_size = past_key_value[0].shape[-2]
	key_states = torch.cat([past_key_value[0], key_states], dim=-2)
	value_states = torch.cat([past_key_value[1], value_states], dim=-2)
	# FoT additionally stores position_ids to support long inputs
	position_ids = torch.cat([past_key_value[2], position_ids], dim=-2)

	if attention_mask.shape[-1] != key_states.shape[-2] and attention_mask.shape[-2] != query_states.shape[-2]:
	raise ValueError("attention_mask should be provided for all key_states in local context")

	# local cache is maintained so that it is <= self.max_cache
	# remaining elements are either dropped or go to memory cache
	if key_states.shape[-2] > self.max_cache:
	num_elems_to_drop = past_local_cache_size

	if mem_and_local_cache:
	drop_keys = key_states[:, :, :num_elems_to_drop, :]
	drop_values = value_states[:, :, :num_elems_to_drop, :]
	# as memory mask use the masking of the last key in context
	# attention_mask [bsz, 1, tgt_seq_len, src_seq_len]
	drop_masks = attention_mask[..., -1, :, None]
	drop_masks = drop_masks[:, :, :num_elems_to_drop, :]

	if use_positionals:
	rfst_drop_keys = rotate_as_if_first(drop_keys, self.rotary_emb)
	else:
	rfst_drop_keys = drop_keys
	mem_update = LongLlamaMemCache(
	keys=rfst_drop_keys.to(self.mem_config.cache_dtype),
	values=drop_values.to(self.mem_config.cache_dtype),
	masks=drop_masks,
	)
	if mem_cache is None:
	mem_cache = mem_update
	else:
	mem_cache = mem_apply_update(
	prev_mem_cache=mem_cache, new_mem_content=mem_update, mem_config=self.mem_config
	)

	key_states = key_states[:, :, num_elems_to_drop:, :]
	value_states = value_states[:, :, num_elems_to_drop:, :]
	position_ids = position_ids[:, :, num_elems_to_drop:, :]
	attention_mask = attention_mask[..., num_elems_to_drop:]

	# FoT additionally stores position_ids to support long inputs
	past_key_value = (key_states, value_states, position_ids) if use_cache else None

	kv_seq_len = key_states.shape[-2]

	if use_positionals:
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	rel_pos_ids = position_ids - torch.min(position_ids, dim=-2, keepdim=True)[0]
	rel_pos_ids = rel_pos_ids.squeeze(3).squeeze(1)

	query_states = rotate_one(query_states, cos, sin, rel_pos_ids[:, -query_states.shape[-2] :])
	key_states = rotate_one(key_states, cos, sin, rel_pos_ids)

	if self.mem_config is not None and self.mem_config.attention_grouping is not None:
	attn_grouping_h, attn_grouping_q = self.mem_config.attention_grouping
	if attn_grouping_h <= 0 or attn_grouping_q <= 0:
	raise ValueError("Attention grouping should be positive")
	else:
	attn_grouping_h, attn_grouping_q = self.num_heads, q_len

	attn_output_h = []
	for beg_h in range(0, self.num_heads, attn_grouping_h):
	end_h = min(beg_h + attn_grouping_h, self.num_heads)

	attn_output_q = []
	for beg_q in range(0, q_len, attn_grouping_q):
	end_q = min(beg_q + attn_grouping_q, q_len)

	if self.config.torch_attention:
	if mem_cache is not None:
	attn_keys = torch.concat(
	[key_states[:, beg_h:end_h], mem_cache.keys[:, beg_h:end_h].to(key_states.dtype)], dim=-2
	)
	attn_values = torch.concat(
	[value_states[:, beg_h:end_h], mem_cache.values[:, beg_h:end_h].to(value_states.dtype)],
	dim=-2,
	)
	mem_mask = mem_cache.masks.squeeze(-1).unsqueeze(-2)
	assert len(mem_mask.shape) == 4
	assert mem_mask.shape[2] == 1
	assert mem_mask.shape[3] == mem_cache.keys.shape[-2]
	mem_mask = torch.broadcast_to(
	mem_mask, (mem_mask.shape[0], mem_mask.shape[1], end_q - beg_q, mem_mask.shape[3])
	)
	attn_mask = torch.concat([attention_mask[:, :, beg_q:end_q], mem_mask], dim=-1)
	assert attn_mask.shape[-1] == attn_keys.shape[-2]
	else:
	attn_keys = key_states[:, beg_h:end_h]
	attn_values = value_states[:, beg_h:end_h]
	attn_mask = attention_mask[:, :, beg_q:end_q]

	attn_queries = query_states[:, beg_h:end_h, beg_q:end_q]

	attn_output = torch.nn.functional.scaled_dot_product_attention(
	query=attn_queries, key=attn_keys, value=attn_values, attn_mask=attn_mask
	)
	attn_output_q.append(attn_output)
	else:
	attn_weights = torch.matmul(
	query_states[:, beg_h:end_h, beg_q:end_q], key_states[:, beg_h:end_h].transpose(2, 3)
	) / math.sqrt(self.head_dim)

	if attn_weights.size() != (bsz, end_h - beg_h, end_q - beg_q, kv_seq_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz, end_h - beg_h, end_q - beg_q, kv_seq_len)}, but is"
	f" {attn_weights.size()}"
	)

	if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights + attention_mask[:, :, beg_q:end_q]
	min_value = (
	torch.finfo(attn_weights.dtype).min
	if -1000000.0 < torch.finfo(attn_weights.dtype).min
	else -1000000.0
	)
	attn_weights = torch.max(
	attn_weights, torch.tensor(min_value, device=attn_weights.device, dtype=attn_weights.dtype)
	)

	if mem_cache is not None:
	mem_mask = mem_cache.masks.squeeze(-1).unsqueeze(-2)
	mem_attn_weights = torch.matmul(
	query_states[:, beg_h:end_h, beg_q:end_q],
	mem_cache.keys[:, beg_h:end_h].transpose(2, 3).to(key_states.dtype),
	) / math.sqrt(self.head_dim)

	assert mem_mask.shape[2] == 1
	mem_attn_weights = mem_attn_weights + mem_mask
	min_value = (
	torch.finfo(mem_attn_weights.dtype).min
	if -1000000.0 < torch.finfo(mem_attn_weights.dtype).min
	else -1000000.0
	)
	mem_attn_weights = torch.max(
	mem_attn_weights,
	torch.tensor(min_value, device=mem_attn_weights.device, dtype=mem_attn_weights.dtype),
	)

	attn_weights = torch.concat([attn_weights, mem_attn_weights], dim=-1)
	combined_value_states = torch.concat(
	[value_states[:, beg_h:end_h], mem_cache.values[:, beg_h:end_h].to(value_states.dtype)],
	dim=-2,
	)
	else:
	combined_value_states = value_states[:, beg_h:end_h]
	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
	query_states.dtype
	)
	attn_output = torch.matmul(attn_weights, combined_value_states)
	assert attn_output.shape[-2] == end_q - beg_q
	attn_output_q.append(attn_output)
	attn_output_h.append(torch.concat(attn_output_q, dim=-2))

	attn_output = torch.concat(attn_output_h, dim=-3)

	if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

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

	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	if mem_no_local_cache:
	if mem_cache is not None:
	mem_cache = mem_apply_update(
	prev_mem_cache=mem_cache, new_mem_content=mem_update, mem_config=self.mem_config
	)
	else:
	mem_cache = mem_update

	return attn_output, attn_weights, past_key_value, mem_cache


	# Modified transformers.models.llama.modeling_llama.LlamaDecoderLayer
	class LongLlamaDecoderLayer(nn.Module):
	def __init__(self, config: LongLlamaConfig, mem_config: Optional[LongLlamaMemConfig] = None):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.self_attn = LongLlamaAttention(config=config, mem_config=mem_config)
	self.mlp = LongLlamaMLP(
	hidden_size=self.hidden_size,
	intermediate_size=config.intermediate_size,
	hidden_act=config.hidden_act,
	)
	self.input_layernorm = LongLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = LongLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	mem_cache: Optional[LongLlamaMemCache] = None,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`, optional): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
	(see `past_key_values`).
	past_key_value (`Tuple(torch.FloatTensor)`, optional): cached past key and value projection states
	along with information about positions
	mem_cache (`LongLlamaMemCache`, optional): memory cache for specific layers
	"""

	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)

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

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

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs + (mem_cache,)


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

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

	Parameters:
	config ([`LongLlamaConfig`]):
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	[`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""
	LONGLLAMA_MEML_DOCSTRING = r"""
	mem_layers ([`int`], optional):
	Indices of layers to be augmented with memory, if None then parameters from config will be used
	mem_dtype (`str`, optional):
	Keys and values will be casted to this type for storage.

	"""


	@add_start_docstrings(
	"The bare LongLLaMA Model outputting raw hidden-states without any specific head on top.",
	LONGLLAMA_START_DOCSTRING,
	)
	# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->LongLlama
	class LongLlamaPreTrainedModel(PreTrainedModel):
	config_class = LongLlamaConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["LongLlamaDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

	def _init_weights(self, module):
	std = self.config.initializer_range
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, LongLlamaModel):
	module.gradient_checkpointing = value


	LONGLLAMA_COMMON_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
	`past_key_values`).

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

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

	[What are position IDs?](../glossary#position-ids)
	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`
	or memory cache is enabled):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
	`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 1 additional tensor of shape
	`(batch_size, 1, sequence_length, 1)`. For memory enriched layers it also contains content of memory cache.
	It is padded with empty tensors so when returned it alwyas has 6 elements.

	Contains pre-computed hidden-states (key and values in the self-attention blocks)
	that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail. This is NOT supported in LongLlamaForCausalLM and LongLlamaForSequenceClassification
	due to the specific input processing.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	LONGLLAMA_MODEL_INPUTS_DOCSTRING = r"""
	mem_caches (`tuple(LongLlamaMemCache)`, optional)
	Memory caches for specified layers, None for others
	"""

	LONGLLAMA_ADD_INPUTS_DOCSTRING = r"""
	last_context_length (`int`, optional)
	Useful for generation, specifies number of tokens that won't be loaded to memory and
	will be left for generation cache
	"""


	def _prepare_pos_ids(past_key_values, batch_size, input_length, device):
	if past_key_values is not None:
	# take previous max pos_id + 1
	if past_key_values[0][2].shape[0] != batch_size:
	raise ValueError(
	f"first dimension of past_key_values should match batch size: {batch_size}"
	f"but got {past_key_values[0][2].shape[0]}"
	)
	next_pos = torch.max(past_key_values[0][2].view(batch_size, -1), dim=-1)[0] + 1
	next_pos = next_pos.view(batch_size, 1)
	else:
	next_pos = torch.zeros(batch_size, 1, device=device, dtype=torch.long)

	position_ids = torch.arange(0, input_length, dtype=torch.long, device=device).view(1, input_length)
	position_ids = position_ids + next_pos
	return position_ids


	@add_start_docstrings(
	"The bare LongLLaMA Model outputting raw hidden-states without any specific head on top.",
	LONGLLAMA_START_DOCSTRING,
	LONGLLAMA_MEML_DOCSTRING,
	)
	# Modified transformers.models.llama.modeling_llama.LlamaModel
	class LongLlamaModel(LongLlamaPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`LongLlamaDecoderLayer`]

	Args:
	config: LlamaConfig
	"""

	def __init__(self, config: LongLlamaConfig):
	super().__init__(config)
	self.mem_layers = config.mem_layers
	self.mem_config = LongLlamaMemConfig(
	positionals=config.mem_positionals,
	cache_dtype=getattr(torch, config.mem_dtype),
	attention_grouping=config.mem_attention_grouping,
	)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

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

	for mem_layer_id in self.mem_layers:
	if mem_layer_id < 0 or mem_layer_id >= config.num_hidden_layers:
	raise ValueError(
	f"Memory layer ids should be between 0 and {config.num_hidden_layers}, got {mem_layer_id}"
	)

	layers = []
	for layer_id in range(config.num_hidden_layers):
	if layer_id in self.mem_layers:
	layer = LongLlamaDecoderLayer(config, mem_config=self.mem_config)
	else:
	layer = LongLlamaDecoderLayer(config, mem_config=None)
	layers.append(layer)

	self.layers = nn.ModuleList(layers)
	self.norm = LongLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.embed_tokens

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

	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(
	input_shape,
	inputs_embeds.dtype,
	device=inputs_embeds.device,
	past_key_values_length=past_key_values_length,
	)

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
	inputs_embeds.device
	)
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask

	@add_start_docstrings_to_model_forward(LONGLLAMA_COMMON_INPUTS_DOCSTRING, LONGLLAMA_MODEL_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	mem_caches: Optional[Tuple[Optional[LongLlamaMemCache]]] = None,
	) -> Union[Tuple, LongLlamaModelOutputWithPast]:
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

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

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

	seq_length_with_past = seq_length
	past_key_values_length = 0

	if past_key_values is not None:
	past_key_values_length = past_key_values[0][0].shape[-2]
	seq_length_with_past = seq_length_with_past + past_key_values_length

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = _prepare_pos_ids(past_key_values, batch_size, seq_length, device)
	else:
	position_ids = position_ids.view(-1, seq_length).long()

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)
	# embed positions
	if attention_mask is None:
	attention_mask = torch.ones(
	(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
	)
	attention_mask = self._prepare_decoder_attention_mask(
	attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
	)

	hidden_states = inputs_embeds

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	next_decoder_cache = ()
	next_mem_caches = ()
	for idx, decoder_layer in enumerate(self.layers):
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	past_key_value = past_key_values[idx] if past_key_values is not None else None
	mem_cache = mem_caches[idx] if mem_caches else None

	if mem_cache is not None and idx not in self.mem_layers:
	raise ValueError("Memory cache provided for a non-memory leayer")

	if (
	self.gradient_checkpointing
	and self.training
	and mem_cache is None
	and idx % self.config.gradient_checkpoint_every_ith == 0
	):

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(*inputs, output_attentions, None, mem_cache=None)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	None,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	mem_cache=mem_cache,
	)

	new_mem_cache = layer_outputs[-1]
	layer_outputs = layer_outputs[:-1]
	next_mem_caches += (new_mem_cache,)

	hidden_states = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
	else:
	next_decoder_cache += (None,)

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

	hidden_states = self.norm(hidden_states)

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

	next_cache = next_decoder_cache if use_cache else None

	mem_cache_returned = False
	for mem_cache in next_mem_caches:
	if mem_cache is not None:
	mem_cache_returned = True
	next_mem_caches = next_mem_caches if mem_cache_returned else None

	if not return_dict:
	return tuple(
	v
	for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, next_mem_caches]
	if v is not None
	)
	return LongLlamaModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	mem_caches=next_mem_caches,
	)


	def _handle_output_of_past_key_values(outputs):
	# merges local caches and memory caches into one single tuple of past_key_values
	# in order to support generation
	batch_size = outputs.last_hidden_state.shape[0]
	if outputs.past_key_values is None and outputs.mem_caches is None:
	return None

	if outputs.past_key_values is None:
	out_past_key_values = (None,) * len(outputs.mem_caches)
	else:
	out_past_key_values = outputs.past_key_values

	if outputs.mem_caches is None:
	out_mem_caches = (None,) * len(outputs.past_key_values)
	else:
	out_mem_caches = outputs.mem_caches

	device = outputs.last_hidden_state.device
	past_key_values = ()
	for local_cache, mem_cache in zip(out_past_key_values, out_mem_caches):
	layer = ()
	if local_cache is not None:
	assert len(local_cache) == 3
	layer += local_cache
	else:
	layer += (torch.empty(batch_size, 0, 0, 0, device=device),) * 3

	if mem_cache is not None:
	layer += (mem_cache.keys, mem_cache.values, mem_cache.masks)
	else:
	layer += (torch.empty(batch_size, 0, 0, 0, device=device),) * 3

	assert len(layer) == 6

	past_key_values += (layer,)

	return past_key_values


	def _split_past_key_values(past_key_values):
	# splits past_key_values to local cache and memory cache
	local_cache_preset = False
	mem_caches_present = False
	if past_key_values is not None:
	local_caches = ()
	mem_caches = ()
	for layer in past_key_values:
	if len(layer) != 6:
	raise ValueError(
	"Expected elements of past_key_values to contain 6 elements."
	"First 3 describing local cache and last 3 describing memory cache."
	f"Instead got {len(layer)} elements"
	)
	else:
	lk, lv, li, memk, memv, memm = layer
	if lk.shape[-2] != 0:
	local_cache_preset = True
	local_caches += ((lk, lv, li),)
	else:
	local_caches += (None,)

	if memk.shape[-2] != 0:
	mem_caches_present = True
	mem_caches += (LongLlamaMemCache(keys=memk, values=memv, masks=memm),)
	else:
	mem_caches += (None,)

	local_caches = local_caches if local_cache_preset else None
	mem_caches = mem_caches if mem_caches_present else None

	return local_caches, mem_caches


	def _handle_long_input(
	model,
	input_ids,
	attention_mask,
	position_ids,
	past_key_values,
	inputs_embeds,
	use_cache,
	output_attentions,
	output_hidden_states,
	return_dict,
	context_window_length,
	last_context_length,
	):
	if output_attentions:
	logger.warning(
	f"Outputing attentions is not supported in LongLlamaForCausalLM and LongLlamaForSequenceClassification. "
	f"Attention of the last window will be returned"
	)

	past_key_values, mem_caches = _split_past_key_values(past_key_values)

	if past_key_values is not None and use_cache is False:
	raise ValueError("past_key_values it not None should imply use_cache == True")

	if past_key_values is not None:
	initial_past_key_values_length = past_key_values[0][0].shape[-2]
	else:
	initial_past_key_values_length = 0

	if input_ids is not None:
	batch_size, input_length = input_ids.shape
	else:
	batch_size, input_length, _ = inputs_embeds.shape

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = _prepare_pos_ids(past_key_values, batch_size, input_length, device)

	if position_ids.shape != (batch_size, input_length):
	raise ValueError(f"Shape of position_ids [{position_ids}] should match [{batch_size, input_length}]")

	if attention_mask is not None:
	attention_mask = attention_mask[..., -(initial_past_key_values_length + input_length) :]
	if attention_mask is not None and (
	attention_mask.shape != (batch_size, initial_past_key_values_length + input_length)
	):
	raise ValueError(
	"Attention mask should be provided for both the local cache and the input",
	f"Expected shape {(batch_size, initial_past_key_values_length + input_length)},"
	f"got {attention_mask.shape}.",
	)

	# First we load prefix to memory cache
	mem_input_length = max(input_length - last_context_length, 0)
	outputs_list = []
	attn_offset = initial_past_key_values_length
	if mem_input_length > 0:
	for i in range(0, mem_input_length, context_window_length):
	beg, end = i, min(mem_input_length, i + context_window_length)

	if attention_mask is not None:
	if past_key_values is not None:
	local_cache_size = past_key_values[0][0].shape[-2]
	else:
	local_cache_size = 0
	attn_length = attention_mask.shape[-1]
	attn_beg = beg + attn_offset - local_cache_size
	attn_end = end + attn_offset
	assert attn_end <= attn_length
	assert attn_beg >= 0 and attn_end > attn_beg

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn, mem_caches)
	outputs = model(
	input_ids=input_ids[..., beg:end] if input_ids is not None else None,
	attention_mask=attention_mask[..., attn_beg:attn_end] if attention_mask is not None else None,
	position_ids=position_ids[..., beg:end],
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds[..., beg:end, :] if inputs_embeds is not None else None,
	use_cache=False if past_key_values is None else use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	mem_caches=mem_caches,
	)
	if i > 0:
	if mem_caches is not None and past_key_values is None:
	for mc_layer in mem_caches:
	if mc_layer is not None:
	del mc_layer.keys
	del mc_layer.values
	del mc_layer.masks

	mem_caches = outputs.mem_caches
	outputs.mem_caches = None
	past_key_values = outputs.past_key_values
	outputs.past_key_values = None
	outputs_list.append(outputs)

	remaining_input_length = input_length - mem_input_length
	beg = mem_input_length
	attn_length = remaining_input_length
	if past_key_values is not None:
	attn_length += past_key_values[0][0].shape[-2]
	attention_mask = attention_mask[..., -attn_length:] if attention_mask is not None else None

	outputs = model(
	input_ids=input_ids[..., beg:] if input_ids is not None else None,
	attention_mask=attention_mask,
	position_ids=position_ids[..., beg:],
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds[..., beg:, :] if inputs_embeds is not None else None,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	mem_caches=mem_caches,
	)

	outputs_list.append(outputs)

	past_key_values = _handle_output_of_past_key_values(outputs_list[-1])

	if output_hidden_states:
	hidden_states = ()
	for hd in zip(*[x.hidden_states for x in outputs_list]):
	hidden_states += (torch.cat(hd, dim=-2),)
	else:
	hidden_states = None

	outputs = BaseModelOutputWithPast(
	last_hidden_state=torch.concat([x.last_hidden_state for x in outputs_list], dim=-2),
	past_key_values=past_key_values,
	hidden_states=hidden_states,
	attentions=outputs_list[-1].attentions,
	)

	if not return_dict:
	outputs = tuple(
	v
	for v in [outputs.last_hidden_state, outputs.past_key_values, outputs.hidden_states, outputs.attentions]
	if v is not None
	)
	return outputs


	# Modified transformers.models.llama.modeling_llama.LlamaForCausalLM
	class LongLlamaForCausalLM(LongLlamaPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.context_window_length = config.max_position_embeddings

	self.model = LongLlamaModel(config)

	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

	def get_input_embeddings(self):
	return self.model.embed_tokens

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

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

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

	def get_decoder(self):
	return self.model

	def _has_generation_cache(self, past_key_values):
	if past_key_values is not None:
	assert len(past_key_values[0]) == 6
	return past_key_values[0][0].shape[-2] != 0

	return False

	@add_start_docstrings_to_model_forward(LONGLLAMA_COMMON_INPUTS_DOCSTRING, LONGLLAMA_ADD_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	last_context_length: Optional[int] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	Args:
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, LlamaForCausalLM

	>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
	>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

	>>> prompt = "Hey, are you conscious? Can you talk to me?"
	>>> inputs = tokenizer(prompt, return_tensors="pt")

	>>> # Generate
	>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
	>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
	```"""
	last_context_length = (
	last_context_length if last_context_length is not None else self.config.last_context_length
	)
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = _handle_long_input(
	model=self.model,
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	context_window_length=self.context_window_length,
	last_context_length=last_context_length,
	)

	hidden_states = outputs[0]
	logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# Enable model parallelism
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)

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

	return CausalLMOutputWithPast(
	loss=loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)

	def prepare_inputs_for_generation(
	self,
	input_ids,
	past_key_values=None,
	attention_mask=None,
	inputs_embeds=None,
	last_context_length=None,
	**kwargs,
	):
	if self._has_generation_cache(past_key_values):
	input_ids = input_ids[:, -1:]

	position_ids = kwargs.get("position_ids", None)
	if attention_mask is not None and position_ids is None:
	# create position_ids on the fly for batch generation
	position_ids = attention_mask.long().cumsum(-1) - 1
	position_ids.masked_fill(position_ids < 0, 0)
	if self._has_generation_cache(past_key_values):
	position_ids = position_ids[:, -1].unsqueeze(-1)

	# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
	if inputs_embeds is not None and past_key_values is None:
	model_inputs = {"inputs_embeds": inputs_embeds}
	else:
	model_inputs = {"input_ids": input_ids}

	model_inputs.update(
	{
	"position_ids": position_ids,
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	"attention_mask": attention_mask,
	"last_context_length": last_context_length,
	}
	)
	return model_inputs

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
	)
	return reordered_past


	@add_start_docstrings(
	"""
	The LongLLaMA Model transformer with a sequence classification head on top (linear layer).

	[`LongLlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
	(e.g. GPT-2) do.

	Since it does classification on the last token, it requires to know the position of the last token. If a
	`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
	no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
	padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
	each row of the batch).
	""",
	LONGLLAMA_START_DOCSTRING,
	LONGLLAMA_MEML_DOCSTRING,
	)
	# Modified from transformers.models.llama.modeling_llama.LlamaForSequenceClassification
	class LongLlamaForSequenceClassification(LongLlamaPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.context_window_length = config.max_position_embeddings
	self.model = LongLlamaModel(config)
	self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

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

	def get_input_embeddings(self):
	return self.model.embed_tokens

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

	@add_start_docstrings_to_model_forward(LONGLLAMA_COMMON_INPUTS_DOCSTRING, LONGLLAMA_ADD_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	last_context_length: Optional[int] = None,
	) -> Union[Tuple, SequenceClassifierOutputWithPast]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	last_context_length = (
	last_context_length if last_context_length is not None else self.config.last_context_length
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	transformer_outputs = _handle_long_input(
	model=self.model,
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	context_window_length=self.context_window_length,
	last_context_length=last_context_length,
	)

	hidden_states = transformer_outputs[0]
	logits = self.score(hidden_states)

	if input_ids is not None:
	batch_size = input_ids.shape[0]
	else:
	batch_size = inputs_embeds.shape[0]

	if self.config.pad_token_id is None and batch_size != 1:
	raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
	else:
	sequence_lengths = -1

	pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

	loss = None
	if labels is not None:
	labels = labels.to(logits.device)
	if self.config.problem_type is None:
	if self.num_labels == 1:
	self.config.problem_type = "regression"
	elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
	self.config.problem_type = "single_label_classification"
	else:
	self.config.problem_type = "multi_label_classification"

	if self.config.problem_type == "regression":
	loss_fct = MSELoss()
	if self.num_labels == 1:
	loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
	else:
	loss = loss_fct(pooled_logits, labels)
	elif self.config.problem_type == "single_label_classification":
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
	elif self.config.problem_type == "multi_label_classification":
	loss_fct = BCEWithLogitsLoss()
	loss = loss_fct(pooled_logits, labels)
	if not return_dict:
	output = (pooled_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return SequenceClassifierOutputWithPast(
	loss=loss,
	logits=pooled_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)