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BlueLM VS llama

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# coding=utf-8

# 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 BlueLM model."""

""" PyTorch LLaMA model."""

import math

import warnings

from typing import List, Optional, Tuple, Union

import torch

import torch.nn.functional as F

import torch.utils.checkpoint

from torch import nn

from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN

from ...activations import ACT2FN

from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast

from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask

from transformers.modeling_utils import PreTrainedModel

from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast

from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings

from ...modeling_utils import PreTrainedModel

from .configuration_bluelm import BlueLMConfig

from ...pytorch_utils import ALL_LAYERNORM_LAYERS

from ...utils import (

add_start_docstrings,

add_start_docstrings_to_model_forward,

is_flash_attn_2_available,

logging,

replace_return_docstrings,

)

from ...utils.import_utils import is_torch_fx_available

from .configuration_llama import LlamaConfig

try:

if is_flash_attn_2_available():

from xformers import ops as xops

from flash_attn import flash_attn_func, flash_attn_varlen_func

except ImportError:

from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa

xops = None

# print("xformers is not installed correctly.")

try:

from apex.normalization import MixedFusedRMSNorm

# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.

except ImportError:

# It means that the function will not be traced through and simply appear as a node in the graph.

MixedFusedRMSNorm = None

if is_torch_fx_available():

# print("Please install nvidia apex from source (https://github.com/NVIDIA/apex#linux) or use ngc container.")

_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "BlueLMConfig"

_CONFIG_FOR_DOC = "LlamaConfig"

def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):

def _get_unpad_data(attention_mask):

"""

seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)

Make causal mask used for bi-directional self-attention.

indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()

"""

max_seqlen_in_batch = seqlens_in_batch.max().item()

bsz, tgt_len = input_ids_shape

cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))

mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))

return (

mask_cond = torch.arange(mask.size(-1))

indices,

mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)

cu_seqlens,

mask = mask.to(dtype)

max_seqlen_in_batch,

)

if past_key_values_length > 0:

mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)

return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):

"""

warnings.warn(

Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.

"Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils.AttentionMaskConverter._prepare_4d_attention_mask"

"""

)

bsz, src_len = mask.size()

return AttentionMaskConverter._prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)

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)

def _make_causal_mask(

input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0

warnings.warn(

"Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"

)

return AttentionMaskConverter._make_causal_mask(

input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length

)

class BlueLMRMSNorm(nn.Module):

class LlamaRMSNorm(nn.Module):

def __init__(self, hidden_size, eps=1e-6):

"""

BlueLMRMSNorm is equivalent to T5LayerNorm

LlamaRMSNorm is equivalent to T5LayerNorm

"""

super().__init__()

self.weight = nn.Parameter(torch.ones(hidden_size))

self.variance_epsilon = eps

def forward(self, hidden_states):

variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)

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)

# convert into half-precision if necessary

if self.weight.dtype in [torch.float16, torch.bfloat16]:

hidden_states = hidden_states.to(self.weight.dtype)

return self.weight * hidden_states

ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)

class BlueLMRotaryEmbedding(torch.nn.Module):

class LlamaRotaryEmbedding(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.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.max_seq_len_cached = max_position_embeddings

self._set_cos_sin_cache(

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

seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()

)

def _set_cos_sin_cache(self, seq_len, device, dtype):

self.max_seq_len_cached = seq_len

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

freqs = torch.einsum("i,j->ij", t, self.inv_freq)

# Different from paper, but it uses a different permutation in order to obtain the same calculation

emb = torch.cat((freqs, freqs), dim=-1)

self.register_buffer("cos_cached", emb.cos()[None, :, None, :], persistent=False)

self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)

self.register_buffer("sin_cached", emb.sin()[None, :, None, :], 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]

# 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

self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

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

freqs = torch.einsum("i,j->ij", t, self.inv_freq)

# Different from paper, but it uses a different permutation in order to obtain the same calculation

emb = torch.cat((freqs, freqs), dim=-1).to(x.device)

self.register_buffer("cos_cached", emb.cos()[None, :, None, :], persistent=False)

self.register_buffer("sin_cached", emb.sin()[None, :, None, :], persistent=False)

return (

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

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

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

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

)

class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):

"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

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

self.scaling_factor = scaling_factor

super().__init__(dim, max_position_embeddings, base, device)

def _set_cos_sin_cache(self, seq_len, device, dtype):

self.max_seq_len_cached = seq_len

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

t = t / self.scaling_factor

freqs = torch.einsum("i,j->ij", t, self.inv_freq)

# Different from paper, but it uses a different permutation in order to obtain the same calculation

emb = torch.cat((freqs, freqs), dim=-1)

self.register_buffer("cos_cached", emb.cos().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.einsum("i,j->ij", t, self.inv_freq)

# Different from paper, but it uses a different permutation in order to obtain the same calculation

emb = torch.cat((freqs, freqs), dim=-1)

self.register_buffer("cos_cached", emb.cos().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, offset: int = 0):

def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):

cos = cos[:, offset : q.shape[1] + offset, ...]

"""Applies Rotary Position Embedding to the query and key tensors.

sin = sin[:, offset : q.shape[1] + offset, ...]

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

class BlueLMMLP(nn.Module):

class LlamaMLP(nn.Module):

def __init__(

def __init__(self, config):

self,

hidden_size: int,

intermediate_size: int,

hidden_act: str,

dropout_prob: float,

super().__init__()

self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)

self.config = config

self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)

self.hidden_size = config.hidden_size

self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)

self.intermediate_size = config.intermediate_size

self.act_fn = ACT2FN[hidden_act]

self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)

self.dropout = nn.Dropout(dropout_prob)

self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)

self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

self.act_fn = ACT2FN[config.hidden_act]

def forward(self, x):

return self.dropout(self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)))

if self.config.pretraining_tp > 1:

slice = self.intermediate_size // self.config.pretraining_tp

gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)

up_proj_slices = self.up_proj.weight.split(slice, dim=0)

down_proj_slices = self.down_proj.weight.split(slice, dim=1)

gate_proj = torch.cat(

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

)

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

intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)

down_proj = [

F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)

]

down_proj = sum(down_proj)

else:

down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

return down_proj

class BlueLMAttention(nn.Module):

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__(

def __init__(self, config: LlamaConfig):

self,

hidden_size: int,

num_heads: int,

dropout_prob: float,

super().__init__()

self.hidden_size = hidden_size

self.config = config

self.num_heads = num_heads

self.hidden_size = config.hidden_size

self.head_dim = hidden_size // num_heads

self.num_heads = config.num_attention_heads

self.dropout_prob = dropout_prob

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 * num_heads) != self.hidden_size:

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`: {num_heads})."

f" and `num_heads`: {self.num_heads})."

)

self.q_proj = nn.Linear(

self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)

hidden_size,

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

num_heads * self.head_dim,

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

bias=False,

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

)

self._init_rope()

self.k_proj = nn.Linear(

hidden_size,

def _init_rope(self):

num_heads * self.head_dim,

if self.config.rope_scaling is None:

bias=False,

self.rotary_emb = LlamaRotaryEmbedding(

)

self.head_dim,

self.v_proj = nn.Linear(

max_position_embeddings=self.max_position_embeddings,

hidden_size,

base=self.rope_theta,

num_heads * self.head_dim,

)

bias=False,

else:

)

scaling_type = self.config.rope_scaling["type"]

self.o_proj = nn.Linear(

scaling_factor = self.config.rope_scaling["factor"]

num_heads * self.head_dim,

if scaling_type == "linear":

hidden_size,

self.rotary_emb = LlamaLinearScalingRotaryEmbedding(

bias=False,

self.head_dim,

)

max_position_embeddings=self.max_position_embeddings,

self.rotary_emb = BlueLMRotaryEmbedding(self.head_dim)

scaling_factor=scaling_factor,

if xops is not None:

base=self.rope_theta,

self.causal_mask = xops.LowerTriangularMask()

)

elif scaling_type == "dynamic":

self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(

self.head_dim,

max_position_embeddings=self.max_position_embeddings,

scaling_factor=scaling_factor,

base=self.rope_theta,

)

else:

raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):

return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).contiguous()

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,

attention_mask: Optional[torch.Tensor] = None,

output_attentions: bool = False,

use_cache: bool = False,

**kwargs,

) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

"""Input shape: Batch x Time x Channel"""

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()

query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)

if self.config.pretraining_tp > 1:

key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)

key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp

value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)

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)

kv_seq_len = key_states.shape[1]

query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]

offset = 0

query_states = torch.cat(query_states, dim=-1)

key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]

key_states = torch.cat(key_states, dim=-1)

value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]

value_states = torch.cat(value_states, dim=-1)

else:

query_states = self.q_proj(hidden_states)

key_states = self.k_proj(hidden_states)

value_states = self.v_proj(hidden_states)

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

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

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

kv_seq_len = key_states.shape[-2]

if past_key_value is not None:

offset = past_key_value[0].shape[1]

kv_seq_len += past_key_value[0].shape[-2]

kv_seq_len += offset

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

query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, offset=offset)

query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

# [bsz, t, nh, hd]

if past_key_value is not None:

# reuse k, v, self_attention

key_states = torch.cat([past_key_value[0], key_states], dim=1)

key_states = torch.cat([past_key_value[0], key_states], dim=2)

value_states = torch.cat([past_key_value[1], value_states], dim=1)

value_states = torch.cat([past_key_value[1], value_states], dim=2)

past_key_value = (key_states, value_states) if use_cache else None

if xops is not None and self.training:

key_states = repeat_kv(key_states, self.num_key_value_groups)

attn_weights = None

value_states = repeat_kv(value_states, self.num_key_value_groups)

attn_output = xops.memory_efficient_attention(

query_states, key_states, value_states, attn_bias=self.causal_mask, p=self.dropout_prob,

attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

op=xops.fmha.MemoryEfficientAttentionFlashAttentionOp

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()}"

)

else:

# [bsz, t, nh, hd]

attn_weights = torch.einsum("bqnh,bknh->bnqk", query_states, key_states) / math.sqrt(self.head_dim)

if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):

if attention_mask is not None:

if attention_mask.size() != (bsz, 1, 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"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"

f" {attn_weights.size()}"

)

attn_weights = attn_weights + attention_mask

if attention_mask is not None:

if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):

raise ValueError(

f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"

)

attn_weights = attn_weights + attention_mask

attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

# upcast attention to fp32

attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)

attn_output = torch.einsum("bnqk,bknh->bqnh", attn_weights, value_states)

attn_output = torch.matmul(attn_weights, value_states)

if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):

if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):

raise ValueError(

f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"

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)

attn_output = self.o_proj(attn_output)

if self.config.pretraining_tp > 1:

attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)

o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)

attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])

else:

attn_output = self.o_proj(attn_output)

if not output_attentions:

attn_weights = None

return attn_output, attn_weights, past_key_value

class BlueLMDecoderLayer(nn.Module):

class LlamaFlashAttention2(LlamaAttention):

def __init__(self, config: BlueLMConfig):

"""

super().__init__()

Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays

self.hidden_size = config.hidden_size

untouched. The only required change would be on the forward pass where it needs to correctly call the public API of

self.self_attn = BlueLMAttention(

flash attention and deal with padding tokens in case the input contains any of them.

hidden_size=self.hidden_size,

"""

num_heads=config.num_attention_heads,

dropout_prob=0,

)

self.mlp = BlueLMMLP(

hidden_size=self.hidden_size,

intermediate_size=config.intermediate_size,

hidden_act=config.hidden_act,

dropout_prob=0,

)

if MixedFusedRMSNorm is None:

self.input_layernorm = BlueLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.post_attention_layernorm = BlueLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

else:

self.input_layernorm = MixedFusedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.post_attention_layernorm = MixedFusedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

def forward(

self,

hidden_states: torch.Tensor,

attention_mask: Optional[torch.Tensor] = None,

attention_mask: Optional[torch.LongTensor] = None,

output_attentions: Optional[bool] = False,

position_ids: Optional[torch.LongTensor] = None,

use_cache: Optional[bool] = False,

past_key_value: Optional[Tuple[torch.Tensor]] = None,

) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:

output_attentions: bool = False,

"""

use_cache: bool = False,

Args:

**kwargs,

hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`

) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

attention_mask (`torch.FloatTensor`, *optional*): attention mask of size

# LlamaFlashAttention2 attention does not support output_attentions

`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.

if "padding_mask" in kwargs:

output_attentions (`bool`, *optional*):

warnings.warn(

Whether or not to return the attentions tensors of all attention layers. See `attentions` under

"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"

returned tensors for more detail.

)

use_cache (`bool`, *optional*):

If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding

(see `past_key_values`).

past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states

"""

residual = hidden_states

hidden_states = self.input_layernorm(hidden_states)

# overwrite attention_mask with padding_mask

attention_mask = kwargs.pop("padding_mask")

# Self Attention

output_attentions = False

hidden_states, self_attn_weights, present_key_value = self.self_attn(

hidden_states=hidden_states,

past_key_value=past_key_value,

attention_mask=attention_mask,

output_attentions=output_attentions,

use_cache=use_cache,

)

hidden_states = residual + hidden_states

# Fully Connected

bsz, q_len, _ = hidden_states.size()

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,)

query_states = self.q_proj(hidden_states)

key_states = self.k_proj(hidden_states)

value_states = self.v_proj(hidden_states)

if output_attentions:

# Flash attention requires the input to have the shape

outputs += (self_attn_weights,)

# batch_size x seq_length x head_dim x hidden_dim

# therefore we just need to keep the original shape

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)

if use_cache:

kv_seq_len = key_states.shape[-2]

outputs += (present_key_value,)

if past_key_value is not None:

kv_seq_len += past_key_value[0].shape[-2]

return outputs

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

query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

BlueLM_START_DOCSTRING = r"""

if past_key_value is not None:

This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the

# reuse k, v, self_attention

library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads

key_states = torch.cat([past_key_value[0], key_states], dim=2)

etc.)

value_states = torch.cat([past_key_value[1], value_states], dim=2)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.

past_key_value = (key_states, value_states) if use_cache else None

Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage

and behavior.

Parameters:

query_states = query_states.transpose(1, 2)

config ([`BlueLMConfig`]):

key_states = key_states.transpose(1, 2)

Model configuration class with all the parameters of the model. Initializing with a config file does not

value_states = value_states.transpose(1, 2)

load the weights associated with the model, only the configuration. Check out the

[`~PreTrainedModel.from_pretrained`] method to load the model weights.

"""

# TODO: llama does not have dropout in the config??

# It is recommended to use dropout with FA according to the docs

# when training.

dropout_rate = 0.0 # if not self.training else self.attn_dropout

@add_start_docstrings(

# In PEFT, usually we cast the layer norms in float32 for training stability reasons

"The bare BlueLM Model outputting raw hidden-states without any specific head on top.",

# therefore the input hidden states gets silently casted in float32. Hence, we need

BlueLM_START_DOCSTRING,

# cast them back in the correct dtype just to be sure everything works as expected.

)

# This might slowdown training & inference so it is recommended to not cast the LayerNorms

class BlueLMPreTrainedModel(PreTrainedModel):

# in fp32. (LlamaRMSNorm handles it correctly)

config_class = BlueLMConfig

base_model_prefix = "model"

supports_gradient_checkpointing = True

_no_split_modules = ["BlueLMDecoderLayer"]

_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

def _init_weights(self, module):

input_dtype = query_states.dtype

std = self.config.initializer_range

if input_dtype == torch.float32:

if isinstance(module, nn.Linear):

# Handle the case where the model is quantized

# module.weight.data.normal_(mean=0.0, std=std)

if hasattr(self.config, "_pre_quantization_dtype"):

torch.nn.init.xavier_normal_(module.weight.data)

target_dtype = self.config._pre_quantization_dtype

if module.bias is not None:

module.bias.data.zero_()

elif isinstance(module, nn.Embedding):

if self.config.use_stable_embedding:

torch.nn.init.xavier_normal_(module.weight.data)

else:

module.weight.data.normal_(mean=0.0, std=std)

target_dtype = self.q_proj.weight.dtype

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, BlueLMModel):

module.gradient_checkpointing = value

BlueLM_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**.

past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):

Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape

`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape

`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention

blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that

don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all

`decoder_input_ids` of shape `(batch_size, sequence_length)`.

inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):

Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This

is useful if you want more control over how to convert `input_ids` indices into associated vectors than the

model's internal embedding lookup matrix.

use_cache (`bool`, *optional*):

If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see

`past_key_values`).

output_attentions (`bool`, *optional*):

Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned

tensors for more detail.

output_hidden_states (`bool`, *optional*):

Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for

more detail.

return_dict (`bool`, *optional*):

Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

"""

@add_start_docstrings(

"The bare BlueLM Model outputting raw hidden-states without any specific head on top.",

BlueLM_START_DOCSTRING,

)

class BlueLMModel(BlueLMPreTrainedModel):

"""

Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BlueLMDecoderLayer`]

Args:

config: BlueLMConfig

"""

def __init__(self, config: BlueLMConfig):

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)

if config.use_stable_embedding:

self.embed_layer_norm = nn.LayerNorm(config.hidden_size,eps=config.rms_norm_eps)

else:

self.embed_layer_norm = None

self.layers = nn.ModuleList([BlueLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])

if MixedFusedRMSNorm is None:

self.norm = BlueLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

else:

self.norm = MixedFusedRMSNorm(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, past_key_values_length=past_key_values_length

).to(inputs_embeds.device)

if attention_mask is not None:

logger.warning_once(

# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]

f"The input hidden states seems to be silently casted in float32, this might be related to"

expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(

f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"

inputs_embeds.device

f" {target_dtype}."

)

combined_attention_mask = (

expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask

)

return combined_attention_mask

query_states = query_states.to(target_dtype)

key_states = key_states.to(target_dtype)

def forward(

value_states = value_states.to(target_dtype)

self,

input_ids: torch.LongTensor = None,

attention_mask: Optional[torch.Tensor] = 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]:

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

attn_output = self._flash_attention_forward(

[`PreTrainedTokenizer.__call__`] for details.

query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate

)

[What are input IDs?](../glossary#input-ids)

attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()

attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):

attn_output = self.o_proj(attn_output)

Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

- 1 for tokens that are **not masked**,

if not output_attentions:

- 0 for tokens that are **masked**.

attn_weights = None

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

return attn_output, attn_weights, past_key_value

past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):

Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of

shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of

def _flash_attention_forward(

self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None

"""

Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token

first unpad the input, then computes the attent

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# Copyright 2023 vivo.
#
# 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 BlueLM model."""
import math
from typing import List, Optional, Tuple, Union

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

from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_bluelm import BlueLMConfig

try:
    from xformers import ops as xops
except ImportError:
    xops = None
    # print("xformers is not installed correctly.")

try:
    from apex.normalization import MixedFusedRMSNorm
except ImportError:
    MixedFusedRMSNorm = None
    # print("Please install nvidia apex from source (https://github.com/NVIDIA/apex#linux) or use ngc container.")

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "BlueLMConfig"

def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, 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))
    mask_cond = torch.arange(mask.size(-1))
    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), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

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

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

inverted_mask = 1.0 - expanded_mask

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

class BlueLMRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        BlueLMRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

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

# convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

return self.weight * hidden_states

class BlueLMRotaryEmbedding(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, persistent=False)

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

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

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

def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
    cos = cos[:, offset : q.shape[1] + offset, ...]
    sin = sin[:, offset : q.shape[1] + offset, ...]
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

class BlueLMMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        dropout_prob: float,
    ):
        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]
        self.dropout = nn.Dropout(dropout_prob)

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

class BlueLMAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        dropout_prob: float,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.dropout_prob = dropout_prob

if (self.head_dim * 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`: {num_heads})."
            )
        self.q_proj = nn.Linear(
            hidden_size,
            num_heads * self.head_dim,
            bias=False,
        )
        self.k_proj = nn.Linear(
            hidden_size,
            num_heads * self.head_dim,
            bias=False,
        )
        self.v_proj = nn.Linear(
            hidden_size,
            num_heads * self.head_dim,
            bias=False,
        )
        self.o_proj = nn.Linear(
            num_heads * self.head_dim,
            hidden_size,
            bias=False,
        )
        self.rotary_emb = BlueLMRotaryEmbedding(self.head_dim)
        if xops is not None:
            self.causal_mask = xops.LowerTriangularMask()

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

def forward(
        self,
        hidden_states: torch.Tensor,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

bsz, q_len, _ = hidden_states.size()

query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)

kv_seq_len = key_states.shape[1]
        offset = 0
        if past_key_value is not None:
            offset = past_key_value[0].shape[1]
            kv_seq_len += offset
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, offset=offset)
        # [bsz, t, nh, hd]

if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=1)
            value_states = torch.cat([past_key_value[1], value_states], dim=1)

past_key_value = (key_states, value_states) if use_cache else None

if xops is not None and self.training:
            attn_weights = None
            attn_output = xops.memory_efficient_attention(
                query_states, key_states, value_states, attn_bias=self.causal_mask, p=self.dropout_prob,
                op=xops.fmha.MemoryEfficientAttentionFlashAttentionOp
            )
        else:
            # [bsz, t, nh, hd]
            attn_weights = torch.einsum("bqnh,bknh->bnqk", query_states, key_states) / 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
                attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

# upcast attention to fp32
            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
            attn_output = torch.einsum("bnqk,bknh->bqnh", attn_weights, value_states)

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

attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

attn_output = self.o_proj(attn_output)

if not output_attentions:
            attn_weights = None

return attn_output, attn_weights, past_key_value

class BlueLMDecoderLayer(nn.Module):
    def __init__(self, config: BlueLMConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = BlueLMAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            dropout_prob=0,
        )
        self.mlp = BlueLMMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            dropout_prob=0,
        )
        if MixedFusedRMSNorm is None:
            self.input_layernorm = BlueLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
            self.post_attention_layernorm = BlueLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.input_layernorm = MixedFusedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
            self.post_attention_layernorm = MixedFusedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

residual = hidden_states

hidden_states = self.input_layernorm(hidden_states)

# Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=past_key_value,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            use_cache=use_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

BlueLM_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 ([`BlueLMConfig`]):
            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 BlueLM Model outputting raw hidden-states without any specific head on top.",
    BlueLM_START_DOCSTRING,
)
class BlueLMPreTrainedModel(PreTrainedModel):
    config_class = BlueLMConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BlueLMDecoderLayer"]
    _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)
            torch.nn.init.xavier_normal_(module.weight.data)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            if self.config.use_stable_embedding:
                torch.nn.init.xavier_normal_(module.weight.data)
            else:
                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, BlueLMModel):
            module.gradient_checkpointing = value

BlueLM_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**.

past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

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

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

@add_start_docstrings(
    "The bare BlueLM Model outputting raw hidden-states without any specific head on top.",
    BlueLM_START_DOCSTRING,
)
class BlueLMModel(BlueLMPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BlueLMDecoderLayer`]

Args:
        config: BlueLMConfig
    """

def __init__(self, config: BlueLMConfig):
        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)
        if config.use_stable_embedding:
            self.embed_layer_norm = nn.LayerNorm(config.hidden_size,eps=config.rms_norm_eps)
        else:
            self.embed_layer_norm = None
        self.layers = nn.ModuleList([BlueLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        if MixedFusedRMSNorm is None:
            self.norm = BlueLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = MixedFusedRMSNorm(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, past_key_values_length=past_key_values_length
            ).to(inputs_embeds.device)

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

def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = 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]:
        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**.

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

If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            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`).
            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.
            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.
        """
        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[1]
            seq_length_with_past = seq_length_with_past + past_key_values_length
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
            if self.embed_layer_norm:
                inputs_embeds = self.embed_layer_norm(inputs_embeds)
        # embed positions
        if xops is not None and self.training:
            attention_mask = None
        else:
            if attention_mask is None:
                attention_mask = torch.ones(
                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
                )
            attention_mask = self._prepare_decoder_attention_mask(
                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
            )

hidden_states = inputs_embeds

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

# decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

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

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

if self.gradient_checkpointing and self.training:

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

return custom_forward

layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    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)

# 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 BlueLMForCausalLM(BlueLMPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]

def __init__(self, config):
        super().__init__(config)
        self.model = BlueLMModel(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

@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,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        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)
            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

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

If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            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]`.
            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.

Returns:

Example:

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

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

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

>>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
        ```"""

output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            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]
        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/pipeline 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, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -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(
            {
                "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) for past_state in layer_past),)
        return reordered_past

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

[`BlueLMForSequenceClassification`] 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).
    """,
    BlueLM_START_DOCSTRING,
)
class BlueLMForSequenceClassification(BlueLMPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]

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

transformer_outputs = self.model(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

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

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

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

loss = None
        if labels is not None:
            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,
        )

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

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    logging,
    replace_return_docstrings,
)
from ...utils.import_utils import is_torch_fx_available
from .configuration_llama import LlamaConfig

if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

# 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():
    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "LlamaConfig"

def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )

def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    warnings.warn(
        "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils.AttentionMaskConverter._prepare_4d_attention_mask"
    )
    return AttentionMaskConverter._prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)

def _make_causal_mask(
    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    warnings.warn(
        "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
    )
    return AttentionMaskConverter._make_causal_mask(
        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
    )

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.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().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

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

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)

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

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]

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

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

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

return down_proj

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

class LlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

def __init__(self, config: LlamaConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.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()

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

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

bsz, q_len, _ = hidden_states.size()

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

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

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

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

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

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

kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

past_key_value = (key_states, value_states) if use_cache else None

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_output = torch.matmul(attn_weights, value_states)

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

attn_output = attn_output.transpose(1, 2).contiguous()

attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

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

if not output_attentions:
            attn_weights = None

return attn_output, attn_weights, past_key_value

class LlamaFlashAttention2(LlamaAttention):
    """
    Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        # LlamaFlashAttention2 attention does not support output_attentions
        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.`"
            )

# overwrite attention_mask with padding_mask
            attention_mask = kwargs.pop("padding_mask")

output_attentions = False

bsz, q_len, _ = hidden_states.size()

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

# Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        # therefore we just need to keep the original shape
        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)

kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

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

query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

past_key_value = (key_states, value_states) if use_cache else None

query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

# TODO: llama does not have dropout in the config??
        # It is recommended to use dropout with FA according to the docs
        # when training.
        dropout_rate = 0.0  # if not self.training else self.attn_dropout

# In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (LlamaRMSNorm handles it correctly)

input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            # Handle the case where the model is quantized
            if hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

attn_output = self._flash_attention_forward(
            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
        )

attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
        attn_output = self.o_proj(attn_output)

if not output_attentions:
            attn_weights = None

return attn_output, attn_weights, past_key_value

def _flash_attention_forward(
        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`int`, *optional*):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
        """
        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=self.is_causal,
            )

attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            attn_output = flash_attn_func(
                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=self.is_causal
            )

return attn_output

def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )

class LlamaDecoderLayer(nn.Module):
    def __init__(self, config: LlamaConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = (
            LlamaAttention(config=config)
            if not getattr(config, "_flash_attn_2_enabled", False)
            else LlamaFlashAttention2(config=config)
        )
        self.mlp = LlamaMLP(config)
        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        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.`"
            )

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

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.)

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 = 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.

- 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`).

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

[What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.

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.
"""

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

Args:
        config: LlamaConfig
    """

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

self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.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

@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
        )
        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 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")

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

if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0)

if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

if getattr(self.config, "_flash_attn_2_enabled", False):
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        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
            )

# 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
        next_decoder_cache = () if use_cache else None

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

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

if self.gradient_checkpointing and self.training:
                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)

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

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)

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

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

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

def get_output_embeddings(self):
        return self.lm_head

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

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

def get_decoder(self):
        return self.model

@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> 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."
        ```"""

# 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
        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

def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]

# Some generation methods already pass only the last input ID
            if input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = input_ids.shape[1] - 1

input_ids = input_ids[:, remove_prefix_length:]

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[:, -input_ids.shape[1] :]

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

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

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

Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    LLAMA_START_DOCSTRING,
)
class LlamaForSequenceClassification(LlamaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = LlamaModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

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

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

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

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

transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

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

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