modeling_qwen.py

# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from transformers.generation.utils import *
import importlib
import math
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.cuda.amp import autocast

from torch.nn import CrossEntropyLoss
from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
from transformers.generation.logits_process import LogitsProcessorList

if TYPE_CHECKING:
    from transformers.generation.streamers import BaseStreamer
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import (
    ModelOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

try:
    from einops import rearrange
except ImportError:
    rearrange = None
from torch import nn

SUPPORT_CUDA = torch.cuda.is_available()
SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7

from .configuration_qwen import QWenConfig
from .qwen_generation_utils import (
    HistoryType,
    make_context,
    decode_tokens,
    get_stop_words_ids,
    StopWordsLogitsProcessor,
)
from .visual import VisionTransformer


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "qwen"
_CONFIG_FOR_DOC = "QWenConfig"

QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]

_ERROR_BAD_CHAT_FORMAT = """\
We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。
如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。
"""

_SENTINEL = object()
_ERROR_STREAM_IN_CHAT = """\
Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
"""

apply_rotary_emb_func = None
rms_norm = None

@dataclass
class CLSModelOutputWithPast(ModelOutput):
    last_hidden_state: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    images_embed: Optional[Tuple[torch.FloatTensor]] = None
    
@dataclass
class CausalLMOutputWithPast(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    cls_logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

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


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

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

    inverted_mask = 1.0 - expanded_mask

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


class QWenAttention(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
        self.seq_length = config.seq_length

        self.hidden_size = config.hidden_size
        self.split_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads

        self.scale_attn_weights = True

        self.projection_size = config.kv_channels * config.num_attention_heads

        assert self.projection_size % config.num_attention_heads == 0
        self.hidden_size_per_attention_head = (
            self.projection_size // config.num_attention_heads
        )

        self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)

        self.c_proj = nn.Linear(
            config.hidden_size, self.projection_size, bias=not config.no_bias
        )

        self.is_fp32 = not (config.bf16 or config.fp16)
        self.bf16 = config.bf16

        self.use_dynamic_ntk = config.use_dynamic_ntk
        self.use_logn_attn = config.use_logn_attn

        logn_list = [
            math.log(i, self.seq_length) if i > self.seq_length else 1
            for i in range(1, 32768)
        ]
        self.logn_tensor = torch.tensor(logn_list)[None, :, None, None]

        self.attn_dropout = nn.Dropout(config.attn_dropout_prob)

    def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None):
        attn_weights = torch.matmul(query, key.transpose(-1, -2))

        if self.scale_attn_weights:
            attn_weights = attn_weights / torch.full(
                [],
                value.size(-1) ** 0.5,
                dtype=attn_weights.dtype,
                device=attn_weights.device,
            )

        query_length, key_length = query.size(-2), key.size(-2)
        # causal_mask = self.bias[
        #     :, :, key_length - query_length : key_length, :key_length
        # ]
        # mask_value = torch.finfo(attn_weights.dtype).min
        # mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(
        #     attn_weights.device
        # )
        # attn_weights = torch.where(
        #     causal_mask, attn_weights.to(attn_weights.dtype), mask_value
        # )
        attn_weights = attn_weights + attention_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        attn_weights = attn_weights.type(value.dtype)
        attn_weights = self.attn_dropout(attn_weights)

        if head_mask is not None:
            attn_weights = attn_weights * head_mask

        attn_output = torch.matmul(attn_weights, value)
        attn_output = attn_output.transpose(1, 2)

        return attn_output, attn_weights

    def _upcast_and_reordered_attn(
        self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None
    ):
        bsz, num_heads, q_seq_len, dk = query.size()
        _, _, k_seq_len, _ = key.size()

        attn_weights = torch.empty(
            bsz * num_heads,
            q_seq_len,
            k_seq_len,
            dtype=torch.float32,
            device=query.device,
        )

        scale_factor = 1.0
        if self.scale_attn_weights:
            scale_factor /= float(value.size(-1)) ** 0.5

        with autocast(enabled=False):
            q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(
                -1, dk, k_seq_len
            )
            attn_weights = torch.baddbmm(
                attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor
            )
            attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)

        query_length, key_length = query.size(-2), key.size(-2)
        causal_mask = registered_causal_mask[
            :, :, key_length - query_length : key_length, :key_length
        ]
        mask_value = torch.finfo(attn_weights.dtype).min
        mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(
            attn_weights.device
        )
        attn_weights = torch.where(causal_mask, attn_weights, mask_value)

        if attention_mask is not None:
            attn_weights = attn_weights + attention_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if attn_weights.dtype != torch.float32:
            raise RuntimeError(
                "Error with upcasting, attn_weights does not have dtype torch.float32"
            )
        attn_weights = attn_weights.type(value.dtype)
        attn_weights = self.attn_dropout(attn_weights)

        if head_mask is not None:
            attn_weights = attn_weights * head_mask

        attn_output = torch.matmul(attn_weights, value)

        return attn_output, attn_weights

    def _split_heads(self, tensor, num_heads, attn_head_size):
        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
        tensor = tensor.view(new_shape)
        return tensor

    def _merge_heads(self, tensor, num_heads, attn_head_size):
        tensor = tensor.contiguous()
        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
        return tensor.view(new_shape)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        rotary_pos_emb: Optional[List[torch.Tensor]] = None,
        registered_causal_mask: Optional[torch.Tensor] = None,
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ):

        mixed_x_layer = self.c_attn(hidden_states)

        query, key, value = mixed_x_layer.split(self.split_size, dim=2)

        query = self._split_heads(query, self.num_heads, self.head_dim)
        key = self._split_heads(key, self.num_heads, self.head_dim)
        value = self._split_heads(value, self.num_heads, self.head_dim)

        if rotary_pos_emb is not None:
            cur_len = query.shape[1]
            rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
            rotary_pos_emb = (rotary_pos_emb,) * 2
            q_pos_emb, k_pos_emb = rotary_pos_emb
            # Slice the pos emb for current inference
            query = apply_rotary_pos_emb(query, q_pos_emb)
            key = apply_rotary_pos_emb(key, k_pos_emb)

        if layer_past is not None:
            past_key, past_value = layer_past[0], layer_past[1]
            key = torch.cat((past_key, key), dim=1)
            value = torch.cat((past_value, value), dim=1)

        if use_cache:
            present = (key, value)
        else:
            present = None

        if self.use_logn_attn and not self.training:
            if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
                self.logn_tensor = self.logn_tensor.to(query.device).type_as(query)
            seq_start = key.size(1) - query.size(1)
            seq_end = key.size(1)
            logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :]
            query = query * logn_tensor.expand_as(query)

        query = query.permute(0, 2, 1, 3)
        key = key.permute(0, 2, 1, 3)
        value = value.permute(0, 2, 1, 3)
        attn_output, attn_weight = self._attn(
            query, key, value, registered_causal_mask, attention_mask, head_mask
        )
        context_layer = self._merge_heads(
            attn_output, self.num_heads, self.head_dim
        )

        attn_output = self.c_proj(context_layer)

        outputs = (attn_output, present)
        if output_attentions:
            outputs += (attn_weight,)

        return outputs


class QWenMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.w1 = nn.Linear(
            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
        )
        self.w2 = nn.Linear(
            config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
        )
        ff_dim_in = config.intermediate_size // 2
        self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)

    def forward(self, hidden_states):
        a1 = self.w1(hidden_states)
        a2 = self.w2(hidden_states)
        intermediate_parallel = a1 * F.silu(a2)
        output = self.c_proj(intermediate_parallel)
        return output

class QWenBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        hidden_size = config.hidden_size
        self.bf16 = config.bf16

        self.ln_1 = RMSNorm(
            hidden_size,
            eps=config.layer_norm_epsilon,
        )
        self.attn = QWenAttention(config)
        self.ln_2 = RMSNorm(
            hidden_size,
            eps=config.layer_norm_epsilon,
        )

        self.mlp = QWenMLP(config)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        rotary_pos_emb: Optional[List[torch.Tensor]] = None,
        registered_causal_mask: Optional[torch.Tensor] = None,
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ):
        layernorm_output = self.ln_1(hidden_states)

        attn_outputs = self.attn(
            layernorm_output,
            rotary_pos_emb,
            registered_causal_mask=registered_causal_mask,
            layer_past=layer_past,
            attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]

        outputs = attn_outputs[1:]

        residual = hidden_states
        layernorm_input = attn_output + residual

        layernorm_output = self.ln_2(layernorm_input)

        residual = layernorm_input
        mlp_output = self.mlp(layernorm_output)
        hidden_states = residual + mlp_output

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        return outputs


class QWenPreTrainedModel(PreTrainedModel):
    config_class = QWenConfig
    base_model_prefix = "transformer"
    is_parallelizable = False
    supports_gradient_checkpointing = True
    _no_split_modules = ["QWenBlock"]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, RMSNorm):
            module.weight.data.fill_(1.0)

        for name, p in module.named_parameters():
            if name == "c_proj.weight":
                p.data.normal_(
                    mean=0.0,
                    std=(
                        self.config.initializer_range
                        / math.sqrt(2 * self.config.num_hidden_layers)
                    ),
                )

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


class QWenModel(QWenPreTrainedModel):
    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]

    def __init__(self, config):
        super().__init__(config)
        self.vocab_size = config.vocab_size
        self.num_hidden_layers = config.num_hidden_layers
        self.embed_dim = config.hidden_size

        self.gradient_checkpointing = False
        self.use_dynamic_ntk = config.use_dynamic_ntk
        self.seq_length = config.seq_length

        self.wte = nn.Embedding(self.vocab_size, self.embed_dim)

        self.drop = nn.Dropout(config.emb_dropout_prob)

        if config.rotary_pct == 1.0:
            self.rotary_ndims = None
        else:
            assert config.rotary_pct < 1
            self.rotary_ndims = int(
                config.kv_channels * config.rotary_pct
            )
        dim = (
            self.rotary_ndims
            if self.rotary_ndims is not None
            else config.kv_channels
        )
        self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)

        self.use_flash_attn = config.use_flash_attn
        self.is_fp32 = not (config.bf16 or config.fp16)
        self.registered_causal_mask = None
        # if (
        #     self.use_flash_attn
        #     and flash_attn_unpadded_func is not None
        #     and not self.is_fp32
        # ):
        #     self.registered_causal_mask = None
        # else:
        #     max_positions = config.max_position_embeddings
        #     self.register_buffer(
        #         "registered_causal_mask",
        #         torch.tril(
        #             torch.ones((max_positions, max_positions), dtype=torch.bool)
        #         ).view(1, 1, max_positions, max_positions),
        #         persistent=False,
        #     )

        self.h = nn.ModuleList(
            [
                QWenBlock(
                    config
                )
                for i in range(config.num_hidden_layers)
            ]
        )
        self.ln_f = RMSNorm(
            self.embed_dim,
            eps=config.layer_norm_epsilon,
        )

        self.visual = VisionTransformer(**config.visual)

        self.post_init()

    def get_input_embeddings(self):
        return self.wte

    def set_input_embeddings(self, new_embeddings):
        self.wte = new_embeddings
    
    # 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 forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']):
            bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
            eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
            assert (bos_pos[0] == eos_pos[0]).all()
            img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
            images = []
            for i, a, b in img_pos:
                image = input_ids[i][a + 1 : b - 1].tolist()
                image = image[ : image.index(self.config.visual['image_start_id'] + 2)]
                images.append(bytes(image).decode('utf-8'))

            images = self.visual.encode(images)
            assert images.shape[0] == len(images)
            fake_images = None
        elif self.training:
            fake_images=torch.zeros(1,3,224,224).to(
                dtype=self.visual.conv1.weight.dtype, device=self.visual.conv1.weight.device)
            images = self.visual(fake_images)
        else:
            fake_images = None
            images = None

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

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size = inputs_embeds.shape[0]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, input_shape[-1])
        if position_ids is not None:
            position_ids = position_ids.view(-1, input_shape[-1])

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0][0].size(-2)

        if position_ids is None:
            position_ids = torch.arange(
                past_length,
                input_shape[-1] + past_length,
                dtype=torch.long,
                device=device,
            )
            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

        encoder_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

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

        if batch_size <= 0:
            raise ValueError("batch_size has to be defined and > 0")
        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, input_shape, inputs_embeds, past_length
        )

        hidden_states = inputs_embeds

        kv_seq_len = hidden_states.size()[1]
        if past_key_values[0] is not None:
            # past key values[0][0] shape: bs * seq_len * head_num * dim
            kv_seq_len += past_key_values[0][0].shape[1]
        if (
            self.use_dynamic_ntk
            and kv_seq_len == hidden_states.size()[1]
            and not self.training
        ):
            context_value = math.log(kv_seq_len / self.seq_length, 2) + 1
            ntk_alpha = 2 ** math.ceil(context_value) - 1
            ntk_alpha = max(ntk_alpha, 1)
        else:
            ntk_alpha = self.rotary_emb._ntk_alpha_cached

        rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha)
        for idx in range(len(rotary_pos_emb)):
            rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device)

        hidden_states = self.drop(hidden_states).clone()
        if fake_images is not None:
            hidden_states = hidden_states + images.mean()*0
        elif images is not None:
            for idx, (i, a, b) in enumerate(img_pos):
                hidden_states[i][a + 1 : b] = images[idx]
        output_shape = input_shape + (hidden_states.size(-1),)

        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

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:

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

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    rotary_pos_emb,
                    self.registered_causal_mask,
                    None,
                    attention_mask,
                    head_mask[i],
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    rotary_pos_emb=rotary_pos_emb,
                    registered_causal_mask=self.registered_causal_mask,
                    attention_mask=attention_mask,
                    head_mask=head_mask[i],
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

        hidden_states = self.ln_f(hidden_states)
        hidden_states = hidden_states.view(output_shape)
        # Add last hidden state
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict:
            return tuple(
                v for v in [hidden_states, presents, all_hidden_states, images] if v is not None
            )
        
        return CLSModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            images_embed=images,
        )


class QWenForClassification(QWenPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]

    def __init__(self, config):
        super().__init__(config)
        assert (
            config.bf16 + config.fp16 + config.fp32 <= 1
        ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"

        autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0

        if autoset_precision:
            if SUPPORT_BF16:
                logger.warn(
                    "The model is automatically converting to bf16 for faster inference. "
                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
                )
                config.bf16 = True
            elif SUPPORT_FP16:
                logger.warn(
                    "The model is automatically converting to fp16 for faster inference. "
                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
                )
                config.fp16 = True
            else:
                config.fp32 = True

        if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
            logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
        if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
            logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
        if config.fp32:
            if SUPPORT_BF16:
                logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
            elif SUPPORT_FP16:
                logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
        self.gamma = config.gamma
        self.class_names = config.class_name
        self.transformer = QWenModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.cls_head = nn.Linear(config.hidden_size, config.num_labels)
        if config.bf16:
            self.transformer.bfloat16()
            self.lm_head.bfloat16()
            self.cls_head.bfloat16()
        if config.fp16:
            self.transformer.half()
            self.lm_head.half()
            self.cls_head.half()
        self.post_init()

    def get_output_embeddings(self):
        return self.lm_head

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

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
    ):
        token_type_ids = kwargs.get("token_type_ids", None)
        if past_key_values:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

        attention_mask = kwargs.get("attention_mask", None)
        position_ids = kwargs.get("position_ids", None)

        if attention_mask is not None and position_ids is None:
            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)
        else:
            position_ids = None

        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

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

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        cls_labels: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

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

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        cls_hidden_states = transformer_outputs.images_embed
        lm_logits = self.lm_head(hidden_states)

        if cls_hidden_states is not None:
            cls_logits = self.cls_head(cls_hidden_states[:, 0, :])
        else :
            cls_logits = None
        loss = None
        if labels is not None:
            labels = labels.to(lm_logits.device)
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            lm_loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
            )
            cls_loss = loss_fct(
                cls_logits.view(-1, cls_logits.size(-1)), cls_labels.view(-1)
            )
            loss = self.gamma*lm_loss + (1-self.gamma)*cls_loss
            print("llm_loss:\t{}\tcls_loss:\t{}".format(lm_loss.item(), cls_loss.item()))
        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=lm_logits,
            cls_logits=cls_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    @staticmethod
    def _reorder_cache(
        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
    ) -> Tuple[Tuple[torch.Tensor]]:

        return tuple(
            tuple(
                past_state.index_select(0, beam_idx.to(past_state.device))
                for past_state in layer_past
            )
            for layer_past in past_key_values
        )

    def chat(
        self,
        tokenizer: PreTrainedTokenizer,
        query: str,
        history: Optional[HistoryType],
        system: str = "You are a helpful assistant.",
        append_history: bool = True,
        stream: Optional[bool] = _SENTINEL,
        stop_words_ids: Optional[List[List[int]]] = None,
        generation_config: Optional[GenerationConfig] = None,
        **kwargs,
    ) -> Tuple[str, HistoryType]:
        generation_config = generation_config if generation_config is not None else self.generation_config

        assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
        assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
        if history is None:
            history = []
        if stop_words_ids is None:
            stop_words_ids = []

        max_window_size = kwargs.get('max_window_size', None)
        if max_window_size is None:
            max_window_size = generation_config.max_window_size
        raw_text, context_tokens = make_context(
            tokenizer,
            query,
            history=history,
            system=system,
            max_window_size=max_window_size,
            chat_format=generation_config.chat_format,
        )

        stop_words_ids.extend(get_stop_words_ids(
            generation_config.chat_format, tokenizer
        ))
        input_ids = torch.tensor([context_tokens]).to(self.device)
        outputs, predicted_class_ids = self.generate(
                    input_ids,
                    stop_words_ids=stop_words_ids,
                    return_dict_in_generate=False,
                    generation_config=generation_config,
                    **kwargs,
                )

        response = decode_tokens(
            outputs[0],
            tokenizer,
            raw_text_len=len(raw_text),
            context_length=len(context_tokens),
            chat_format=generation_config.chat_format,
            verbose=False,
            errors='replace'
        )

        predicted_class_names = self.class_names[predicted_class_ids[0]]

        return response, predicted_class_names

    def chat_stream(
            self,
            tokenizer: PreTrainedTokenizer,
            query: str,
            history: Optional[HistoryType],
            system: str = "You are a helpful assistant.",
            stop_words_ids: Optional[List[List[int]]] = None,
            logits_processor: Optional[LogitsProcessorList] = None,
            generation_config: Optional[GenerationConfig] = None,
            **kwargs,
    ) -> Generator[str, Any, None]:
        generation_config = generation_config if generation_config is not None else self.generation_config
        assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
        if history is None:
            history = []
        if stop_words_ids is None:
            stop_words_ids = []

        max_window_size = kwargs.get('max_window_size', None)
        if max_window_size is None:
            max_window_size = generation_config.max_window_size
        raw_text, context_tokens = make_context(
            tokenizer,
            query,
            history=history,
            system=system,
            max_window_size=max_window_size,
            chat_format=generation_config.chat_format,
        )

        stop_words_ids.extend(get_stop_words_ids(
            generation_config.chat_format, tokenizer
        ))
        if stop_words_ids is not None:
            stop_words_logits_processor = StopWordsLogitsProcessor(
                stop_words_ids=stop_words_ids,
                eos_token_id=generation_config.eos_token_id,
            )
            if logits_processor is None:
                logits_processor = LogitsProcessorList([stop_words_logits_processor])
            else:
                logits_processor.append(stop_words_logits_processor)
        input_ids = torch.tensor([context_tokens]).to(self.device)

        from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
        self.__class__.generate_stream = NewGenerationMixin.generate
        self.__class__.sample_stream = NewGenerationMixin.sample_stream
        stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)

        def stream_generator():
            outputs = []
            for token in self.generate_stream(
                    input_ids,
                    return_dict_in_generate=False,
                    generation_config=stream_config,
                    logits_processor=logits_processor,
                    seed=-1,
                    **kwargs):
                outputs.append(token.item())
                yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore', keep_image_special=True)

        return stream_generator()

    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        prefix_allowed_tokens_fn: Optional[
            Callable[[int, torch.Tensor], List[int]]
        ] = None,
        synced_gpus: Optional[bool] = None,
        assistant_model: Optional["PreTrainedModel"] = None,
        streamer: Optional["BaseStreamer"] = None,
        negative_prompt_ids: Optional[torch.Tensor] = None,
        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        generation_config = generation_config if generation_config is not None else self.generation_config

        # Process stop_words_ids.
        stop_words_ids = kwargs.pop("stop_words_ids", None)
        if stop_words_ids is None and generation_config is not None:
            stop_words_ids = getattr(generation_config, "stop_words_ids", None)
        if stop_words_ids is None:
            stop_words_ids = getattr(generation_config, "stop_words_ids", None)

        if stop_words_ids is not None:
            stop_words_logits_processor = StopWordsLogitsProcessor(
                stop_words_ids=stop_words_ids,
                eos_token_id=generation_config.eos_token_id,
            )
            if logits_processor is None:
                logits_processor = LogitsProcessorList([stop_words_logits_processor])
            else:
                logits_processor.append(stop_words_logits_processor)
        if synced_gpus is None:
            if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
                synced_gpus = True
            else:
                synced_gpus = False

        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
        self._validate_model_class()

        # priority: `generation_config` argument > `model.generation_config` (the default generation config)
        if generation_config is None:
            # legacy: users may modify the model configuration to control generation -- update the generation config
            # model attribute accordingly, if it was created from the model config
            if self.generation_config._from_model_config:
                new_generation_config = GenerationConfig.from_model_config(self.config)
                if new_generation_config != self.generation_config:
                    warnings.warn(
                        "You have modified the pretrained model configuration to control generation. This is a"
                        " deprecated strategy to control generation and will be removed soon, in a future version."
                        " Please use a generation configuration file (see"
                        " https://huggingface.co/docs/transformers/main_classes/text_generation )"
                    )
                    self.generation_config = new_generation_config
            generation_config = self.generation_config

        generation_config = copy.deepcopy(generation_config)
        model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
        generation_config.validate()
        self._validate_model_kwargs(model_kwargs.copy())

        # 2. Set generation parameters if not already defined
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()

        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
            if model_kwargs.get("attention_mask", None) is None:
                logger.warning(
                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
                )
            eos_token_id = generation_config.eos_token_id
            if isinstance(eos_token_id, list):
                eos_token_id = eos_token_id[0]
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
            generation_config.pad_token_id = eos_token_id

        # 3. Define model inputs
        # inputs_tensor has to be defined
        # model_input_name is defined if model-specific keyword input is passed
        # otherwise model_input_name is None
        # all model-specific keyword inputs are removed from `model_kwargs`
        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
            inputs, generation_config.bos_token_id, model_kwargs
        )
        batch_size = inputs_tensor.shape[0]

        # 4. Define other model kwargs
        model_kwargs["output_attentions"] = generation_config.output_attentions
        model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
        # decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
        # generating the first new token or not, and we only want to use the embeddings for the first new token)
        if not self.config.is_encoder_decoder and model_input_name == "inputs_embeds":
            model_kwargs["use_cache"] = True
        else:
            model_kwargs["use_cache"] = generation_config.use_cache

        accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
        requires_attention_mask = "encoder_outputs" not in model_kwargs

        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
            )
        # decoder-only models should use left-padding for generation
        if not self.config.is_encoder_decoder:
            # If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
            # Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
            if (
                generation_config.pad_token_id is not None
                and len(inputs_tensor.shape) == 2
                and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
            ):
                logger.warning(
                    "A decoder-only architecture is being used, but right-padding was detected! For correct "
                    "generation results, please set `padding_side='left'` when initializing the tokenizer."
                )

        if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
            # if model is encoder decoder encoder_outputs are created
            # and added to `model_kwargs`
            model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
                inputs_tensor, model_kwargs, model_input_name
            )

        # 5. Prepare `input_ids` which will be used for auto-regressive generation
        if self.config.is_encoder_decoder:
            input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
                batch_size=batch_size,
                model_input_name=model_input_name,
                model_kwargs=model_kwargs,
                decoder_start_token_id=generation_config.decoder_start_token_id,
                bos_token_id=generation_config.bos_token_id,
                device=inputs_tensor.device,
            )
        else:
            input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")

        if streamer is not None:
            streamer.put(input_ids.cpu())

        # 6. Prepare `max_length` depending on other stopping criteria.
        input_ids_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        if generation_config.max_new_tokens is not None:
            if not has_default_max_length:
                logger.warning(
                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
                    "Please refer to the documentation for more information. "
                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
                )
            generation_config.max_length = generation_config.max_new_tokens + input_ids_length
        self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)

        # 7. determine generation mode
        generation_mode = self._get_generation_mode(generation_config, assistant_model)

        if streamer is not None and (generation_config.num_beams > 1):
            raise ValueError(
                "`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
            )

        if self.device.type != input_ids.device.type:
            warnings.warn(
                "You are calling .generate() with the `input_ids` being on a device type different"
                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
                " Please make sure that you have put `input_ids` to the"
                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
                " running `.generate()`.",
                UserWarning,
            )

        # 8. prepare distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_length,
            encoder_input_ids=inputs_tensor,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            logits_processor=logits_processor,
            model_kwargs=model_kwargs,
            negative_prompt_ids=negative_prompt_ids,
            negative_prompt_attention_mask=negative_prompt_attention_mask,
        )

        # 9. prepare stopping criteria
        stopping_criteria = self._get_stopping_criteria(
            generation_config=generation_config, stopping_criteria=stopping_criteria
        )
        # 11. prepare logits warper
        logits_warper = self._get_logits_warper(generation_config)

        # 12. expand input_ids with `num_return_sequences` additional sequences per batch
        input_ids, model_kwargs = self._expand_inputs_for_generation(
            input_ids=input_ids,
            expand_size=generation_config.num_return_sequences,
            is_encoder_decoder=self.config.is_encoder_decoder,
            **model_kwargs,
        )

        # 13. run sample
        return self.sample(
            input_ids,
            logits_processor=logits_processor,
            logits_warper=logits_warper,
            stopping_criteria=stopping_criteria,
            pad_token_id=generation_config.pad_token_id,
            eos_token_id=generation_config.eos_token_id,
            output_scores=generation_config.output_scores,
            return_dict_in_generate=generation_config.return_dict_in_generate,
            synced_gpus=synced_gpus,
            streamer=streamer,
            **model_kwargs,
        )
    
    def sample(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        logits_warper: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[int]]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: bool = False,
        streamer: Optional["BaseStreamer"] = None,
        **model_kwargs,
    ):
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use"
                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
        logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
        if isinstance(eos_token_id, int):
            eos_token_id = [eos_token_id]
        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
        output_attentions = (
            output_attentions if output_attentions is not None else self.generation_config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate
            if return_dict_in_generate is not None
            else self.generation_config.return_dict_in_generate
        )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        # keep track of which sequences are already finished
        unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)

        this_peer_finished = False  # used by synced_gpus only
        cls_logits = None
        # auto-regressive generation
        while True:
            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            # prepare model inputs
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # forward pass to get next token
            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]
            if outputs.cls_logits is not None:
                cls_logits = outputs.cls_logits
            # pre-process distribution
            next_token_scores = logits_processor(input_ids, next_token_logits)
            next_token_scores = logits_warper(input_ids, next_token_scores)

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # sample
            probs = nn.functional.softmax(next_token_scores, dim=-1)
            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)

            # finished sentences should have their next token be a padding token
            if eos_token_id is not None:
                if pad_token_id is None:
                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)

            # update generated ids, model inputs, and length for next step
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
            if streamer is not None:
                streamer.put(next_tokens.cpu())
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )

            # if eos_token was found in one sentence, set sentence to finished
            if eos_token_id_tensor is not None:
                unfinished_sequences = unfinished_sequences.mul(
                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
                )

                # stop when each sentence is finished
                if unfinished_sequences.max() == 0:
                    this_peer_finished = True

            # stop if we exceed the maximum length
            if stopping_criteria(input_ids, scores):
                this_peer_finished = True

            if this_peer_finished and not synced_gpus:
                break
        
        if streamer is not None:
            streamer.end()
        predicted_class_ids = torch.argmax(cls_logits, dim=1)

        return input_ids, predicted_class_ids


class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, base=10000):
        super().__init__()
        self.dim = dim
        self.base = base
        self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        if importlib.util.find_spec("einops") is None:
            raise RuntimeError("einops is required for Rotary Embedding")

        self._rotary_pos_emb_cache = None
        self._seq_len_cached = 0
        self._ntk_alpha_cached = 1.0

    def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0):
        seqlen = max_seq_len + offset
        if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
            base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
            self.inv_freq = 1.0 / (
                base
                ** (
                    torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
                    / self.dim
                )
            )
            self._seq_len_cached = max(2 * seqlen, 16)
            self._ntk_alpha_cached = ntk_alpha
            seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
            freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
            
            emb = torch.cat((freqs, freqs), dim=-1)
            from einops import rearrange

            emb = rearrange(emb, "n d -> 1 n 1 d")

            cos, sin = emb.cos(), emb.sin()
            self._rotary_pos_emb_cache = [cos, sin]

    def forward(self, max_seq_len, offset=0, ntk_alpha=1.0):
        self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha)
        cos, sin = self._rotary_pos_emb_cache
        return [cos[:, offset : offset + max_seq_len], sin[:, offset : offset + max_seq_len]]

def _rotate_half(x):
    from einops import rearrange

    x = rearrange(x, "... (j d) -> ... j d", j=2)
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(t, freqs):
    cos, sin = freqs
    if apply_rotary_emb_func is not None and t.is_cuda:
        t_ = t.float()
        cos = cos.squeeze(0).squeeze(1)[:, : cos.shape[-1] // 2]
        sin = sin.squeeze(0).squeeze(1)[:, : sin.shape[-1] // 2]
        output = apply_rotary_emb_func(t_, cos, sin).type_as(t)
        return output
    else:
        rot_dim = freqs[0].shape[-1]
        cos, sin = freqs
        t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
        t_ = t_.float()
        t_pass_ = t_pass_.float()
        t_ = (t_ * cos) + (_rotate_half(t_) * sin)
        return torch.cat((t_, t_pass_), dim=-1).type_as(t)


class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        if rms_norm is not None and x.is_cuda:
            return rms_norm(x, self.weight, self.eps)
        else:
            output = self._norm(x.float()).type_as(x)
            return output * self.weight