diff --git "a/yuan_moe_hf_model.py" "b/yuan_moe_hf_model.py"
--- "a/yuan_moe_hf_model.py"
+++ "b/yuan_moe_hf_model.py"
@@ -1,1454 +1,1454 @@
-# 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 Yuan model."""
-import math
-from typing import List, Optional, Tuple, Union
-import torch.nn.functional as F
-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_yuan import YuanConfig
-from einops import rearrange
-#from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
-#from flash_attn import flash_attn_func
-
-import copy
-
-try:
-    from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
-    from flash_attn import flash_attn_func
-except ImportError:
-    flash_attn_unpadded_func = None
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "YuanConfig"
-
-
-class LocalizedFiltering(torch.nn.Module):
-    """
-    Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
-    variable names and moving away from the stateful representation of incremental decoding state. See
-    "https://arxiv.org/abs/2209.10655" for more details.
-    """
-
-    def __init__(self, hidden_size):
-        super().__init__()
-
-        self.embed_dim = hidden_size
-        self.lf_conv2d_group = 1
-        self.lf_conv2d_num_pad = 1
-
-        self.conv1 = torch.nn.Conv2d(self.embed_dim, self.embed_dim // 2, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
-        self.conv2 = torch.nn.Conv2d(self.embed_dim // 2, self.embed_dim, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
-        self.output_layernorm = YuanRMSNorm(self.embed_dim)
-
-    def _train_forward(self, inputs):
-        inputs = inputs.transpose(0,1)
-        seq_len, bsz, embed_dim = inputs.size()
-        if embed_dim != self.embed_dim:
-            raise ValueError(
-                f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
-            )
-        residual = inputs
-
-        inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
-        output1 = self.conv1(inputs)
-        output1 = output1[:, :, :seq_len, :]
-
-        output2 = self.conv2(output1)
-        output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
-        output2 = output2.view(seq_len, bsz, embed_dim)
-        assert output2.shape == residual.shape
-
-        lf_output = self.output_layernorm(output2 + residual)
-        lf_output = lf_output.transpose(0,1)
-        return lf_output
-
-    def _inference_forward(self, inputs, before_hidden_states):
-
-        if before_hidden_states is None:
-            inputs = inputs.transpose(0,1)
-            seq_len, bsz, embed_dim = inputs.size()
-            if embed_dim != self.embed_dim:
-                raise ValueError(
-                    f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
-                )
-            residual = inputs
-
-            inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
-            output1 = self.conv1(inputs)
-            output1 = output1[:, :, :seq_len, :]
-
-            output2 = self.conv2(output1)
-            output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
-            output2 = output2.view(seq_len, bsz, embed_dim)
-            assert output2.shape == residual.shape
-
-            lf_output = self.output_layernorm(output2 + residual)
-            lf_output = lf_output.transpose(0,1)
-            return lf_output
-        else:
-            inputs = inputs.transpose(0,1)
-            before_hidden_states = before_hidden_states.transpose(0,1)
-            residual = inputs
-
-            seq_len, bsz, embed_dim = inputs.size()
-            seq_len_before, _, _ = before_hidden_states.size()
-
-            assert seq_len == 1 and seq_len_before == 2
-
-            inputs = torch.cat((before_hidden_states, inputs), dim=0)
-            inputs = inputs.view(3, 1, bsz, embed_dim).permute(2, 3, 0, 1)
-
-            output1 = self.conv1(inputs)
-            output2 = self.conv2(output1[:,:,1:-1,:])
-            output2 = output2[:,:,1:-1,:]
-            output2 = output2.view(1, bsz, embed_dim)
-            assert output2.shape == residual.shape
-
-            lf_output = self.output_layernorm(output2 + residual)
-            lf_output = lf_output.transpose(0,1)
-
-        return lf_output
-
-
-
-    def forward(
-        self,
-        inputs,
-        before_hidden_states
-    ) -> torch.Tensor:
-        assert self.lf_conv2d_num_pad == 1
-        if self.training:
-            lf_output = self._train_forward(inputs)
-        else:
-            lf_output = self._inference_forward(inputs, before_hidden_states)
-
-        return lf_output
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-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_0(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    rot_dim = sin.shape[-1]
-
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-
-    q, q_pass = q[..., :rot_dim], q[..., rot_dim:]
-    k, k_pass = k[..., :rot_dim], k[..., rot_dim:]
-
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-
-    return torch.cat((q_embed, q_pass), dim=-1), torch.cat((k_embed, k_pass), dim=-1)
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    #import pdb;pdb.set_trace()
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-class YuanRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        YuanRMSNorm is equivalent to LlamaRMSNorm
-        """
-        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)
-
-class YuanRotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-
-        """
-        YuanRotaryEmbedding is equivalent to LlamaRotaryEmbedding in transformers v4.36
-        """
-
-        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))
-        inv_freq = inv_freq.to(torch.bfloat16)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(
-            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
-        )
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
-
-        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-# flash attn
-class FlashSelfAttention(torch.nn.Module):
-    """Implement the scaled dot product attention with softmax.
-    Arguments
-    ---------
-        softmax_scale: The temperature to use for the softmax attention.
-                      (default: 1/sqrt(d_keys) where d_keys is computed at
-                      runtime)
-        attention_dropout: The dropout rate to apply to the attention
-                           (default: 0.0)
-    """
-    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
-                 device=None, dtype=None):
-        super().__init__()
-        assert flash_attn_unpadded_func is not None, ('Please install FlashAttention first, '
-                                                      'e.g., with pip install flash-attn')
-        assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.dropout_p = attention_dropout
-
-    def forward(self, q, k, v):
-        """Implements the multihead softmax attention.
-        Arguments
-        ---------
-            q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
-        """
-
-        assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v)))
-        assert all((i.is_cuda for i in (q,k,v)))
-
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
-        seqlen_k = k.shape[1]
-
-        q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
-        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
-                                    device=q.device)
-
-        if self.training:
-            # during training q,k,v always have same seqlen
-            assert seqlen_k == seqlen_q
-
-            is_causal = self.causal
-            cu_seqlens_k = cu_seqlens_q
-            dropout_p = self.dropout_p
-        else:
-            # turn off FA causal mask after first inference autoregressive iteration
-            # only on first autoregressive step q,k,v have same seqlen
-            is_causal = seqlen_q == seqlen_k
-            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
-                        device=q.device)
-            dropout_p = 0
-
-        output = flash_attn_unpadded_func(
-            q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
-            dropout_p,
-            softmax_scale=self.softmax_scale, causal=is_causal
-        )
-
-        output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
-        return output
-
-
-class ParallelAttention_router(nn.Module):
-    def __init__(self, config):
-        super(ParallelAttention_router, self).__init__()
-        layer_number=0
-        self.layer_number = max(1, layer_number)
-        
-
-        self.flash_attn_drop = 0.01
-        self.hidden_size = config.hidden_size
-        self.projection_size = config.moe_config['moe_num_experts']            
-            
-        self.query = nn.Linear(self.hidden_size, self.projection_size, bias=False)
-        self.key = nn.Linear(self.hidden_size, self.projection_size, bias=False)
-        self.value = nn.Linear(self.hidden_size, self.projection_size, bias=False)
-
-
-    def forward(self, hidden_states, attention_mask=None, enc_position_ids=None,
-                encoder_output=None, inference_params=None,
-                rotary_pos_emb=None):
-        is_first_step = False
-        before_hidden_states = None
-        
-        query_layer = self.query(hidden_states)
-        key_layer = self.key(hidden_states)
-        value_layer = self.value(hidden_states)
-        
-        b = query_layer.size(0)
-        s = query_layer.size(1) # seq*batch = token_num
-        z = query_layer.size(2) # expert_num
-        
-        # use fp32 router
-        query_layer = query_layer.float().view(b,s,z,1)
-        key_layer = key_layer.float().view(b,s,z,1)
-        value_layer = value_layer.float().view(b,s,z,1)
-        
-        
-        attn_weights = torch.matmul(query_layer, key_layer.transpose(2, 3))
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-        
-        attn_output = torch.matmul(attn_weights, value_layer)
-        
-        router_output = attn_output.view(b*s, z)
-        
-        return router_output
-
-class YuanExpertMLP(nn.Module):
-    def __init__(self, config):
-        super(YuanExpertMLP, self).__init__()
-        
-        self.gated_linear_unit = config.moe_config['gated_linear_unit']
-        self.ffn_hidden_size = config.moe_config['ffn_hidden_size']
-
-
-        if self.gated_linear_unit:
-            self.w1 = nn.Linear(config.hidden_size, self.ffn_hidden_size*2, bias=False)
-            
-              
-        else:
-            self.w1 = nn.Linear(config.hidden_size, self.ffn_hidden_size, bias=False)
-            
-        self.act_fn = ACT2FN[config.hidden_act]
-        self.w2 = nn.Linear(self.ffn_hidden_size, config.hidden_size, bias=False)
-        
-
-    def forward(self, x):
-        x = self.w1(x)
-        if self.gated_linear_unit:
-            x = torch.chunk(x, 2, dim=-1)
-            x = self.act_fn(x[0]) * x[1]
-        else:
-            x = self.act_fn(x)  
-        x = self.w2(x)  
-        return x
-    
-
-
-class YuanMLP(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str
-    ):
-        super().__init__()
-        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
-        self.act_fn = ACT2FN[hidden_act]
-
-    def forward(self, x):
-        return self.down_proj(self.gate_proj(x) * self.act_fn(self.up_proj(x)))
-        
-
-class YuanAttention(nn.Module):
-    """Localized Filtering-based Attention 'YUAN 2.0: A Large Language Model with Localized Filtering-based Attention' paper"""
-
-    def __init__(self, config: YuanConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-
-        try:
-            self.attention_projection_size = config.attention_projection_size
-        except:
-            self.attention_projection_size = None
-        
-        if self.attention_projection_size is None:
-            self.head_dim = self.hidden_size // self.num_heads
-        else:
-            self.head_dim = self.attention_projection_size // self.num_heads
-
-        self.max_position_embeddings = config.max_position_embeddings
-        self.causal_mask = config.causal_mask
-        self.softmax_scale = 1.0 / math.sqrt(self.head_dim)
-        self.use_flash_attention = config.use_flash_attention
-        try:
-            self.use_shareqk = config.use_shareqk
-        except Exception as e:
-            self.use_shareqk=False
-        self.dropout = 0.0
-        
-        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        
-        if self.head_dim == self.hidden_size // self.num_heads:
-            self.rotary_emb = YuanRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
-
-        else:
-            self.rotary_emb = YuanRotaryEmbedding(self.hidden_size // self.num_heads, max_position_embeddings=self.max_position_embeddings)
-
-        if self.use_shareqk:
-            self.qk_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-            self.qk_weight = nn.Parameter(torch.Tensor(2, self.hidden_size))
-            self.qk_bias = nn.Parameter(torch.Tensor(2, self.hidden_size))
-        else:
-            self.lf_gate = LocalizedFiltering(self.hidden_size)
-            self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-            self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        
-        bsz, q_len, _ = hidden_states.size()
-        before_hidden_states = None
-        is_first_step = False
-        if use_cache:
-            if past_key_value is None:
-                inference_hidden_states_memory = torch.empty(bsz, 2, hidden_states.shape[2], dtype=hidden_states.dtype)
-                is_first_step = True
-            else:
-                before_hidden_states = past_key_value[2]
-
-        if use_cache:
-            if is_first_step:
-                if q_len >= 2:
-                    inference_hidden_states_memory = hidden_states[ :, -2:, :]
-                else:
-                    inference_hidden_states_memory[:, :, :] = 0
-                    inference_hidden_states_memory[:, -1:, :] = hidden_states[:, -1:, :]
-            else:
-                hidden_states_tmp = before_hidden_states[:, -1:, :]
-                inference_hidden_states_memory = copy.deepcopy(torch.cat((hidden_states_tmp, hidden_states), dim=1))
-        
-        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        if self.use_shareqk:
-            qk_states = self.qk_proj(hidden_states).view(bsz, q_len, self.num_heads*self.head_dim)
-            query_key = qk_states.unsqueeze(2) * self.qk_weight + self.qk_bias
-            query_states, key_states = torch.unbind(query_key, dim=2)
-
-            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_heads, self.head_dim).transpose(1, 2)
-        else:
-            hidden_states = self.lf_gate(hidden_states,before_hidden_states)
-            query_states = self.q_proj(hidden_states)
-            key_states = self.k_proj(hidden_states)
-            qk_states = torch.cat([query_states, key_states], dim=-1)
-            qk_states = qk_states.view(bsz,q_len,self.num_heads,int(qk_states.shape[-1]//self.num_heads))
-            (query_states,key_states) =  torch.chunk(qk_states, 2, dim=-1)
-            query_states = query_states.transpose(1, 2)
-            key_states = key_states.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_0(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,inference_hidden_states_memory) if use_cache else None 
-        if self.use_flash_attention:
-            attn_weights = None
-            query_states = query_states.transpose(1, 2)
-            key_states = key_states.transpose(1, 2)
-            value_states = value_states.transpose(1, 2)
-
-            batch_size, seqlen_q = query_states.shape[0], query_states.shape[1]
-            seqlen_k = key_states.shape[1]
-
-            q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [query_states, key_states, value_states]]
-
-            cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int,
-                                    device=q.device)
-                                    
-            if self.training:                        
-                assert seqlen_k == seqlen_q
-                cu_seqlens_k = cu_seqlens_q
-                is_causal = self.causal_mask
-            else:
-                is_causal = seqlen_q == seqlen_k
-                cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int,
-                        device=q.device)
-                self.dropout=0
-
-            output = flash_attn_unpadded_func(
-                q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, self.dropout, causal=is_causal
-            )
-
-            attn_output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
-        else:
-            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
-                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.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)
-
-        if self.attention_projection_size is None:
-            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-        else:
-            attn_output = attn_output.reshape(bsz, q_len, self.attention_projection_size)
-        
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-        return attn_output, attn_weights, past_key_value
-
-
-
-class YuanMoeLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.num_experts = config.moe_config['moe_num_experts']
-        self.top_k = config.moe_config['moe_top_k']
-        self.norm_topk_prob = config.moe_config['norm_topk_prob']
-        self.hidden_size = config.hidden_size
-        
-        
-        self.gate = ParallelAttention_router(config)
-        self.experts = nn.ModuleList(
-            [YuanExpertMLP(config) for _ in range(self.num_experts)]
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-        if self.norm_topk_prob:
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
-        )
-
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
-        
-        # Loop over all available experts in the model and perform the computation on each expert
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx])
-
-            if top_x.shape[0] == 0:
-                continue
-
-            # in torch it is faster to index using lists than torch tensors
-            top_x_list = top_x.tolist()
-            idx_list = idx.tolist()
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-        
-        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
-        return final_hidden_states, router_logits
-
-
-class YuanDecoderLayer(nn.Module):
-    def __init__(self, config: YuanConfig):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = YuanAttention(config=config)
-        
-        if config.moe_config['moe_num_experts'] > 0:
-            self.mlp = YuanMoeLayer(config)
-        else:
-            self.mlp = YuanMLP(
-                hidden_size=self.hidden_size,
-                intermediate_size=config.intermediate_size,
-                hidden_act=config.hidden_act,
-            )
-        
-        
-        self.input_layernorm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = YuanRMSNorm(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,
-    ) -> 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,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            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, router_logits = 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
-
-
-YUAN_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 ([`YuanConfig`]):
-            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 Yuan Model outputting raw hidden-states without any specific head on top.",
-   YUAN_START_DOCSTRING,
-)
-class YuanPreTrainedModel(PreTrainedModel):
-    config_class = YuanConfig 
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["YuanDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, YuanModel):
-            module.gradient_checkpointing = value
-
-
-YUAN_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare Yuan Model outputting raw hidden-states without any specific head on top.",
-    YUAN_START_DOCSTRING,
-)
-class YuanModel(YuanPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`YuanDecoderLayer`]
-
-    Args:
-        config: YuanConfig
-    """
-
-    def __init__(self, config: YuanConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        
-        #TODO: control it by config
-        self.eod_token = config.eod_token
-        self.reset_attention_mask = config.reset_attention_mask
-        self.reset_position_ids = config.reset_position_ids
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList([YuanDecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
-    def _prepare_decoder_attention_mask_training(self, input_id, inputs_embeds, eod_token, reset_mask_flag ,reset_attention_mask=True, reset_position_ids=True):
-    
-        micro_batch_size, seq_length = input_id.size()
-        
-        attention_mask = torch.tril(torch.ones(
-            (micro_batch_size, seq_length, seq_length), device=inputs_embeds.device)).view(
-                micro_batch_size, 1, seq_length, seq_length)    
-                
-        position_ids = torch.arange(seq_length, dtype=torch.long,
-                                    device=inputs_embeds.device)
-        position_ids = position_ids.unsqueeze(0).expand_as(input_id)
-               
-        if reset_position_ids:
-            position_ids = position_ids.clone()
-        
-        if reset_position_ids or reset_attention_mask:
-            # Loop through the batches:
-            for b in range(micro_batch_size):
-
-                # Find indecies where EOD token is.
-                eod_index = position_ids[b, input_id[b] == eod_token]
-
-                # Detach indecies from positions if going to modify positions.
-                if reset_position_ids:
-                    eod_index = eod_index.clone()
-                # Loop through EOD indecies:
-                prev_index = 0
-                for j in range(eod_index.size()[0]):
-                    i = eod_index[j]
-                    # Mask attention loss.
-                    if reset_attention_mask:
-                        attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
-                    # Reset positions.
-                    if reset_position_ids:
-                        position_ids[b, (i + 1):] -= (i + 1 - prev_index)
-                        prev_index = i + 1
-                        
-        inverted_mask = 1 - attention_mask   
-        output_attn_mask = inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min)
-        if reset_mask_flag:
-            output_attn_mask = output_attn_mask[:,:,-1:,:]
-        return output_attn_mask, position_ids
-
-    @add_start_docstrings_to_model_forward(YUAN_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,
-        output_router_logits: 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_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-        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
-        input_ids1 = copy.deepcopy(input_ids)
-        reset_mask_flag = False
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-            if use_cache:
-                reset_mask_flag = True
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-
-        if past_key_values is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-        
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-        if self.training or self.reset_position_ids:
-            attention_mask, _ = self._prepare_decoder_attention_mask_training(input_ids1, inputs_embeds, self.eod_token, reset_mask_flag, self.reset_attention_mask, self.reset_position_ids)
-        
-        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,
-                    position_ids,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-
-            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 YuanForCausalLM(YuanPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.eod_token = config.eod_token
-        self.sep_token = config.sep_token
-        self.use_loss_mask = config.use_loss_mask
-        self.model = YuanModel(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-    
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    def get_loss_mask(self, input_ids, labels, eod_token, sep_token):
-        micro_batch_size, seq_length = input_ids.size()
-        loss_mask = torch.ones(input_ids.size(), dtype=torch.float, device=input_ids.device)
-
-        position_ids = torch.arange(seq_length, dtype=torch.long,
-                                device=input_ids.device)
-        position_ids = position_ids.unsqueeze(0).expand_as(input_ids) 
-
-
-        """modify loss_mask to only calculate the loss of the answer (separated with [SEP])"""
-
-        for b in range(micro_batch_size):
-            eod_indexs = position_ids[b, input_ids[b] == eod_token]
-            sep_indexs = position_ids[b, input_ids[b] == sep_token]
-
-            if len(eod_indexs) == 0 or len(sep_indexs) == 0:
-                loss_mask[b] = 1.0
-            else:
-                if eod_indexs[0] > sep_indexs[0]:
-                    loss_mask[b, 0:sep_indexs[0]] = 0
-
-                    if len(eod_indexs) == len(sep_indexs):
-                        for ii, eod_index in enumerate(eod_indexs):
-                            start_index = eod_index
-                            if ii == (len(sep_indexs) - 1):
-                                stop_index = seq_length
-                            else:
-                                stop_index = sep_indexs[ii + 1]
-                            loss_mask[b, start_index:stop_index] = 0.0
-                    else:
-                        if len(eod_indexs) > len(sep_indexs):
-                            loss_mask[b,:] = 1.0
-                        else:
-                            for ii, eod_index in enumerate(eod_indexs):
-                                start_index = eod_index
-                                stop_index = sep_indexs[ii + 1]
-
-                                loss_mask[b, start_index:stop_index] = 0.0
-
-                elif eod_indexs[0] < sep_indexs[0]:
-
-                    if len(eod_indexs) == len(sep_indexs):
-                        for ii, eod_index in enumerate(eod_indexs):
-                            start_index = eod_index
-                            stop_index = sep_indexs[ii]
-                            loss_mask[b, start_index:stop_index] = 0.0
-
-                    else:
-                        if len(eod_indexs) < len(sep_indexs):
-                            loss_mask[b,:] = 1.0
-                        else:
-                            for ii, eod_index in enumerate(eod_indexs):
-                                start_index = eod_index
-                                if ii >= len(sep_indexs):
-                                    stop_index = seq_length
-                                else:
-                                    stop_index = sep_indexs[ii]
-                                loss_mask[b, start_index:stop_index] = 0.0
-
-        loss_mask[input_ids == eod_token] = 1.0
-        return loss_mask                       
-    @add_start_docstrings_to_model_forward(YUAN_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_router_logits: 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, YuanForCausalLM
-
-        >>> model = YuanForCausalLM.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
-        
-        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]
-        
-        logits = self.lm_head(hidden_states)
-        loss = None
-        if labels is not None:
-            if self.use_loss_mask:
-                loss_mask = self.get_loss_mask(input_ids, labels, self.eod_token, self.sep_token)
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            if self.use_loss_mask:
-                loss_fct = CrossEntropyLoss(reduction='none')
-                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)
-                loss = torch.sum(loss * loss_mask) / loss_mask.sum()
-            else:
-                loss_fct = CrossEntropyLoss()
-                shift_logits = shift_logits.view(-1, self.config.vocab_size)
-                shift_labels = shift_labels.view(-1)
-                # Enable model parallelism
-                shift_labels = shift_labels.to(shift_logits.device)
-                loss = loss_fct(shift_logits, shift_labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-        
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
-    ):
-
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past
-
-
-@add_start_docstrings(
-    """
-    The Yuan Model transformer with a sequence classification head on top (linear layer).
-
-    [`YuanForSequenceClassification`] 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).
-    """,
-    YUAN_START_DOCSTRING,
-)
-class YuanForSequenceClassification(YuanPreTrainedModel):
-    #_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 = YuanModel(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(YUAN_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.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-
+# 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 Yuan model."""
+import math
+from typing import List, Optional, Tuple, Union
+import torch.nn.functional as F
+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_yuan import YuanConfig
+from einops import rearrange
+#from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
+#from flash_attn import flash_attn_func
+
+import copy
+
+try:
+    from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
+    from flash_attn import flash_attn_func
+except ImportError:
+    flash_attn_unpadded_func = None
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "YuanConfig"
+
+
+class LocalizedFiltering(torch.nn.Module):
+    """
+    Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
+    variable names and moving away from the stateful representation of incremental decoding state. See
+    "https://arxiv.org/abs/2209.10655" for more details.
+    """
+
+    def __init__(self, hidden_size):
+        super().__init__()
+
+        self.embed_dim = hidden_size
+        self.lf_conv2d_group = 1
+        self.lf_conv2d_num_pad = 1
+
+        self.conv1 = torch.nn.Conv2d(self.embed_dim, self.embed_dim // 2, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
+        self.conv2 = torch.nn.Conv2d(self.embed_dim // 2, self.embed_dim, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
+        self.output_layernorm = YuanRMSNorm(self.embed_dim)
+
+    def _train_forward(self, inputs):
+        inputs = inputs.transpose(0,1)
+        seq_len, bsz, embed_dim = inputs.size()
+        if embed_dim != self.embed_dim:
+            raise ValueError(
+                f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
+            )
+        residual = inputs
+
+        inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+        output1 = self.conv1(inputs)
+        output1 = output1[:, :, :seq_len, :]
+
+        output2 = self.conv2(output1)
+        output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
+        output2 = output2.view(seq_len, bsz, embed_dim)
+        assert output2.shape == residual.shape
+
+        lf_output = self.output_layernorm(output2 + residual)
+        lf_output = lf_output.transpose(0,1)
+        return lf_output
+
+    def _inference_forward(self, inputs, before_hidden_states):
+
+        if before_hidden_states is None:
+            inputs = inputs.transpose(0,1)
+            seq_len, bsz, embed_dim = inputs.size()
+            if embed_dim != self.embed_dim:
+                raise ValueError(
+                    f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
+                )
+            residual = inputs
+
+            inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+            output1 = self.conv1(inputs)
+            output1 = output1[:, :, :seq_len, :]
+
+            output2 = self.conv2(output1)
+            output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
+            output2 = output2.view(seq_len, bsz, embed_dim)
+            assert output2.shape == residual.shape
+
+            lf_output = self.output_layernorm(output2 + residual)
+            lf_output = lf_output.transpose(0,1)
+            return lf_output
+        else:
+            inputs = inputs.transpose(0,1)
+            before_hidden_states = before_hidden_states.transpose(0,1)
+            residual = inputs
+
+            seq_len, bsz, embed_dim = inputs.size()
+            seq_len_before, _, _ = before_hidden_states.size()
+
+            assert seq_len == 1 and seq_len_before == 2
+
+            inputs = torch.cat((before_hidden_states, inputs), dim=0)
+            inputs = inputs.view(3, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+
+            output1 = self.conv1(inputs)
+            output2 = self.conv2(output1[:,:,1:-1,:])
+            output2 = output2[:,:,1:-1,:]
+            output2 = output2.view(1, bsz, embed_dim)
+            assert output2.shape == residual.shape
+
+            lf_output = self.output_layernorm(output2 + residual)
+            lf_output = lf_output.transpose(0,1)
+
+        return lf_output
+
+
+
+    def forward(
+        self,
+        inputs,
+        before_hidden_states
+    ) -> torch.Tensor:
+        assert self.lf_conv2d_num_pad == 1
+        if self.training:
+            lf_output = self._train_forward(inputs)
+        else:
+            lf_output = self._inference_forward(inputs, before_hidden_states)
+
+        return lf_output
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+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_0(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    rot_dim = sin.shape[-1]
+
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+
+    q, q_pass = q[..., :rot_dim], q[..., rot_dim:]
+    k, k_pass = k[..., :rot_dim], k[..., rot_dim:]
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+
+    return torch.cat((q_embed, q_pass), dim=-1), torch.cat((k_embed, k_pass), dim=-1)
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    #import pdb;pdb.set_trace()
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class YuanRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        YuanRMSNorm is equivalent to LlamaRMSNorm
+        """
+        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)
+
+class YuanRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+
+        """
+        YuanRotaryEmbedding is equivalent to LlamaRotaryEmbedding in transformers v4.36
+        """
+
+        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))
+        inv_freq = inv_freq.to(torch.bfloat16)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+# flash attn
+class FlashSelfAttention(torch.nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
+                 device=None, dtype=None):
+        super().__init__()
+        assert flash_attn_unpadded_func is not None, ('Please install FlashAttention first, '
+                                                      'e.g., with pip install flash-attn')
+        assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, q, k, v):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
+        """
+
+        assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v)))
+        assert all((i.is_cuda for i in (q,k,v)))
+
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        seqlen_k = k.shape[1]
+
+        q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
+        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
+                                    device=q.device)
+
+        if self.training:
+            # during training q,k,v always have same seqlen
+            assert seqlen_k == seqlen_q
+
+            is_causal = self.causal
+            cu_seqlens_k = cu_seqlens_q
+            dropout_p = self.dropout_p
+        else:
+            # turn off FA causal mask after first inference autoregressive iteration
+            # only on first autoregressive step q,k,v have same seqlen
+            is_causal = seqlen_q == seqlen_k
+            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
+                        device=q.device)
+            dropout_p = 0
+
+        output = flash_attn_unpadded_func(
+            q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
+            dropout_p,
+            softmax_scale=self.softmax_scale, causal=is_causal
+        )
+
+        output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+        return output
+
+
+class ParallelAttention_router(nn.Module):
+    def __init__(self, config):
+        super(ParallelAttention_router, self).__init__()
+        layer_number=0
+        self.layer_number = max(1, layer_number)
+        
+
+        self.flash_attn_drop = 0.01
+        self.hidden_size = config.hidden_size
+        self.projection_size = config.moe_config['moe_num_experts']            
+            
+        self.query = nn.Linear(self.hidden_size, self.projection_size, bias=False)
+        self.key = nn.Linear(self.hidden_size, self.projection_size, bias=False)
+        self.value = nn.Linear(self.hidden_size, self.projection_size, bias=False)
+
+
+    def forward(self, hidden_states, attention_mask=None, enc_position_ids=None,
+                encoder_output=None, inference_params=None,
+                rotary_pos_emb=None):
+        is_first_step = False
+        before_hidden_states = None
+        
+        query_layer = self.query(hidden_states)
+        key_layer = self.key(hidden_states)
+        value_layer = self.value(hidden_states)
+        
+        b = query_layer.size(0)
+        s = query_layer.size(1) # seq*batch = token_num
+        z = query_layer.size(2) # expert_num
+        
+        # use fp32 router
+        query_layer = query_layer.float().view(b,s,z,1)
+        key_layer = key_layer.float().view(b,s,z,1)
+        value_layer = value_layer.float().view(b,s,z,1)
+        
+        
+        attn_weights = torch.matmul(query_layer, key_layer.transpose(2, 3))
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+        
+        attn_output = torch.matmul(attn_weights, value_layer)
+        
+        router_output = attn_output.view(b*s, z)
+        
+        return router_output
+
+class YuanExpertMLP(nn.Module):
+    def __init__(self, config):
+        super(YuanExpertMLP, self).__init__()
+        
+        self.gated_linear_unit = config.moe_config['gated_linear_unit']
+        self.ffn_hidden_size = config.moe_config['ffn_hidden_size']
+
+
+        if self.gated_linear_unit:
+            self.w1 = nn.Linear(config.hidden_size, self.ffn_hidden_size*2, bias=False)
+            
+              
+        else:
+            self.w1 = nn.Linear(config.hidden_size, self.ffn_hidden_size, bias=False)
+            
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.w2 = nn.Linear(self.ffn_hidden_size, config.hidden_size, bias=False)
+        
+
+    def forward(self, x):
+        x = self.w1(x)
+        if self.gated_linear_unit:
+            x = torch.chunk(x, 2, dim=-1)
+            x = self.act_fn(x[0]) * x[1]
+        else:
+            x = self.act_fn(x)  
+        x = self.w2(x)  
+        return x
+    
+
+
+class YuanMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str
+    ):
+        super().__init__()
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.gate_proj(x) * self.act_fn(self.up_proj(x)))
+        
+
+class YuanAttention(nn.Module):
+    """Localized Filtering-based Attention 'YUAN 2.0: A Large Language Model with Localized Filtering-based Attention' paper"""
+
+    def __init__(self, config: YuanConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+
+        try:
+            self.attention_projection_size = config.attention_projection_size
+        except:
+            self.attention_projection_size = None
+        
+        if self.attention_projection_size is None:
+            self.head_dim = self.hidden_size // self.num_heads
+        else:
+            self.head_dim = self.attention_projection_size // self.num_heads
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.causal_mask = config.causal_mask
+        self.softmax_scale = 1.0 / math.sqrt(self.head_dim)
+        self.use_flash_attention = config.use_flash_attention
+        try:
+            self.use_shareqk = config.use_shareqk
+        except Exception as e:
+            self.use_shareqk=False
+        self.dropout = 0.0
+        
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        
+        if self.head_dim == self.hidden_size // self.num_heads:
+            self.rotary_emb = YuanRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+        else:
+            self.rotary_emb = YuanRotaryEmbedding(self.hidden_size // self.num_heads, max_position_embeddings=self.max_position_embeddings)
+
+        if self.use_shareqk:
+            self.qk_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+            self.qk_weight = nn.Parameter(torch.Tensor(2, self.hidden_size))
+            self.qk_bias = nn.Parameter(torch.Tensor(2, self.hidden_size))
+        else:
+            self.lf_gate = LocalizedFiltering(self.hidden_size)
+            self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+            self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        
+        bsz, q_len, _ = hidden_states.size()
+        before_hidden_states = None
+        is_first_step = False
+        if use_cache:
+            if past_key_value is None:
+                inference_hidden_states_memory = torch.empty(bsz, 2, hidden_states.shape[2], dtype=hidden_states.dtype)
+                is_first_step = True
+            else:
+                before_hidden_states = past_key_value[2]
+
+        if use_cache:
+            if is_first_step:
+                if q_len >= 2:
+                    inference_hidden_states_memory = hidden_states[ :, -2:, :]
+                else:
+                    inference_hidden_states_memory[:, :, :] = 0
+                    inference_hidden_states_memory[:, -1:, :] = hidden_states[:, -1:, :]
+            else:
+                hidden_states_tmp = before_hidden_states[:, -1:, :]
+                inference_hidden_states_memory = copy.deepcopy(torch.cat((hidden_states_tmp, hidden_states), dim=1))
+        
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        if self.use_shareqk:
+            qk_states = self.qk_proj(hidden_states).view(bsz, q_len, self.num_heads*self.head_dim)
+            query_key = qk_states.unsqueeze(2) * self.qk_weight + self.qk_bias
+            query_states, key_states = torch.unbind(query_key, dim=2)
+
+            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_heads, self.head_dim).transpose(1, 2)
+        else:
+            hidden_states = self.lf_gate(hidden_states,before_hidden_states)
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            qk_states = torch.cat([query_states, key_states], dim=-1)
+            qk_states = qk_states.view(bsz,q_len,self.num_heads,int(qk_states.shape[-1]//self.num_heads))
+            (query_states,key_states) =  torch.chunk(qk_states, 2, dim=-1)
+            query_states = query_states.transpose(1, 2)
+            key_states = key_states.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_0(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,inference_hidden_states_memory) if use_cache else None 
+        if self.use_flash_attention:
+            attn_weights = None
+            query_states = query_states.transpose(1, 2)
+            key_states = key_states.transpose(1, 2)
+            value_states = value_states.transpose(1, 2)
+
+            batch_size, seqlen_q = query_states.shape[0], query_states.shape[1]
+            seqlen_k = key_states.shape[1]
+
+            q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [query_states, key_states, value_states]]
+
+            cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int,
+                                    device=q.device)
+                                    
+            if self.training:                        
+                assert seqlen_k == seqlen_q
+                cu_seqlens_k = cu_seqlens_q
+                is_causal = self.causal_mask
+            else:
+                is_causal = seqlen_q == seqlen_k
+                cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int,
+                        device=q.device)
+                self.dropout=0
+
+            output = flash_attn_unpadded_func(
+                q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, self.dropout, causal=is_causal
+            )
+
+            attn_output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+        else:
+            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
+                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.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)
+
+        if self.attention_projection_size is None:
+            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        else:
+            attn_output = attn_output.reshape(bsz, q_len, self.attention_projection_size)
+        
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+        return attn_output, attn_weights, past_key_value
+
+
+
+class YuanMoeLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.moe_config['moe_num_experts']
+        self.top_k = config.moe_config['moe_top_k']
+        self.norm_topk_prob = config.moe_config['norm_topk_prob']
+        self.hidden_size = config.hidden_size
+        
+        
+        self.gate = ParallelAttention_router(config)
+        self.experts = nn.ModuleList(
+            [YuanExpertMLP(config) for _ in range(self.num_experts)]
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        if self.norm_topk_prob:
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # One hot encode the selected experts to create an expert mask
+        # this will be used to easily index which expert is going to be sollicitated
+        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+        
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            idx, top_x = torch.where(expert_mask[expert_idx])
+
+            if top_x.shape[0] == 0:
+                continue
+
+            # in torch it is faster to index using lists than torch tensors
+            top_x_list = top_x.tolist()
+            idx_list = idx.tolist()
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+
+class YuanDecoderLayer(nn.Module):
+    def __init__(self, config: YuanConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = YuanAttention(config=config)
+        
+        if config.moe_config['moe_num_experts'] > 0:
+            self.mlp = YuanMoeLayer(config)
+        else:
+            self.mlp = YuanMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+            )
+        
+        
+        self.input_layernorm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = YuanRMSNorm(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,
+    ) -> 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,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            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, router_logits = 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
+
+
+YUAN_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 ([`YuanConfig`]):
+            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 Yuan Model outputting raw hidden-states without any specific head on top.",
+   YUAN_START_DOCSTRING,
+)
+class YuanPreTrainedModel(PreTrainedModel):
+    config_class = YuanConfig 
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["YuanDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, YuanModel):
+            module.gradient_checkpointing = value
+
+
+YUAN_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Yuan Model outputting raw hidden-states without any specific head on top.",
+    YUAN_START_DOCSTRING,
+)
+class YuanModel(YuanPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`YuanDecoderLayer`]
+
+    Args:
+        config: YuanConfig
+    """
+
+    def __init__(self, config: YuanConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        
+        #TODO: control it by config
+        self.eod_token = config.eod_token
+        self.reset_attention_mask = config.reset_attention_mask
+        self.reset_position_ids = config.reset_position_ids
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([YuanDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def _prepare_decoder_attention_mask_training(self, input_id, inputs_embeds, eod_token, reset_mask_flag ,reset_attention_mask=True, reset_position_ids=True):
+    
+        micro_batch_size, seq_length = input_id.size()
+        
+        attention_mask = torch.tril(torch.ones(
+            (micro_batch_size, seq_length, seq_length), device=inputs_embeds.device)).view(
+                micro_batch_size, 1, seq_length, seq_length)    
+                
+        position_ids = torch.arange(seq_length, dtype=torch.long,
+                                    device=inputs_embeds.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_id)
+               
+        if reset_position_ids:
+            position_ids = position_ids.clone()
+        
+        if reset_position_ids or reset_attention_mask:
+            # Loop through the batches:
+            for b in range(micro_batch_size):
+
+                # Find indecies where EOD token is.
+                eod_index = position_ids[b, input_id[b] == eod_token]
+
+                # Detach indecies from positions if going to modify positions.
+                if reset_position_ids:
+                    eod_index = eod_index.clone()
+                # Loop through EOD indecies:
+                prev_index = 0
+                for j in range(eod_index.size()[0]):
+                    i = eod_index[j]
+                    # Mask attention loss.
+                    if reset_attention_mask:
+                        attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
+                    # Reset positions.
+                    if reset_position_ids:
+                        position_ids[b, (i + 1):] -= (i + 1 - prev_index)
+                        prev_index = i + 1
+                        
+        inverted_mask = 1 - attention_mask   
+        output_attn_mask = inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min)
+        if reset_mask_flag:
+            output_attn_mask = output_attn_mask[:,:,-1:,:]
+        return output_attn_mask, position_ids
+
+    @add_start_docstrings_to_model_forward(YUAN_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,
+        output_router_logits: 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_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        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
+        input_ids1 = copy.deepcopy(input_ids)
+        reset_mask_flag = False
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+            if use_cache:
+                reset_mask_flag = True
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+        
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        if self.training or self.reset_position_ids:
+            attention_mask, _ = self._prepare_decoder_attention_mask_training(input_ids1, inputs_embeds, self.eod_token, reset_mask_flag, self.reset_attention_mask, self.reset_position_ids)
+        
+        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,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            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 YuanForCausalLM(YuanPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.eod_token = config.eod_token
+        self.sep_token = config.sep_token
+        self.use_loss_mask = config.use_loss_mask
+        self.model = YuanModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+    
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def get_loss_mask(self, input_ids, labels, eod_token, sep_token):
+        micro_batch_size, seq_length = input_ids.size()
+        loss_mask = torch.ones(input_ids.size(), dtype=torch.float, device=input_ids.device)
+
+        position_ids = torch.arange(seq_length, dtype=torch.long,
+                                device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids) 
+
+
+        """modify loss_mask to only calculate the loss of the answer (separated with [SEP])"""
+
+        for b in range(micro_batch_size):
+            eod_indexs = position_ids[b, input_ids[b] == eod_token]
+            sep_indexs = position_ids[b, input_ids[b] == sep_token]
+
+            if len(eod_indexs) == 0 or len(sep_indexs) == 0:
+                loss_mask[b] = 1.0
+            else:
+                if eod_indexs[0] > sep_indexs[0]:
+                    loss_mask[b, 0:sep_indexs[0]] = 0
+
+                    if len(eod_indexs) == len(sep_indexs):
+                        for ii, eod_index in enumerate(eod_indexs):
+                            start_index = eod_index
+                            if ii == (len(sep_indexs) - 1):
+                                stop_index = seq_length
+                            else:
+                                stop_index = sep_indexs[ii + 1]
+                            loss_mask[b, start_index:stop_index] = 0.0
+                    else:
+                        if len(eod_indexs) > len(sep_indexs):
+                            loss_mask[b,:] = 1.0
+                        else:
+                            for ii, eod_index in enumerate(eod_indexs):
+                                start_index = eod_index
+                                stop_index = sep_indexs[ii + 1]
+
+                                loss_mask[b, start_index:stop_index] = 0.0
+
+                elif eod_indexs[0] < sep_indexs[0]:
+
+                    if len(eod_indexs) == len(sep_indexs):
+                        for ii, eod_index in enumerate(eod_indexs):
+                            start_index = eod_index
+                            stop_index = sep_indexs[ii]
+                            loss_mask[b, start_index:stop_index] = 0.0
+
+                    else:
+                        if len(eod_indexs) < len(sep_indexs):
+                            loss_mask[b,:] = 1.0
+                        else:
+                            for ii, eod_index in enumerate(eod_indexs):
+                                start_index = eod_index
+                                if ii >= len(sep_indexs):
+                                    stop_index = seq_length
+                                else:
+                                    stop_index = sep_indexs[ii]
+                                loss_mask[b, start_index:stop_index] = 0.0
+
+        loss_mask[input_ids == eod_token] = 1.0
+        return loss_mask                       
+    @add_start_docstrings_to_model_forward(YUAN_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: 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, YuanForCausalLM
+
+        >>> model = YuanForCausalLM.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
+        
+        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]
+        
+        logits = self.lm_head(hidden_states)
+        loss = None
+        if labels is not None:
+            if self.use_loss_mask:
+                loss_mask = self.get_loss_mask(input_ids, labels, self.eod_token, self.sep_token)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            if self.use_loss_mask:
+                loss_fct = CrossEntropyLoss(reduction='none')
+                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)
+                loss = torch.sum(loss * loss_mask) / loss_mask.sum()
+            else:
+                loss_fct = CrossEntropyLoss()
+                shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                shift_labels = shift_labels.view(-1)
+                # Enable model parallelism
+                shift_labels = shift_labels.to(shift_logits.device)
+                loss = loss_fct(shift_logits, shift_labels)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+        
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Yuan Model transformer with a sequence classification head on top (linear layer).
+
+    [`YuanForSequenceClassification`] 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).
+    """,
+    YUAN_START_DOCSTRING,
+)
+class YuanForSequenceClassification(YuanPreTrainedModel):
+    #_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 = YuanModel(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(YUAN_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.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+