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.

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 (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)
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


# 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__()

        max_positions = config.max_position_embeddings
        self.register_buffer(
            "bias",
            torch.tril(
                torch.ones((max_positions, max_positions), dtype=torch.bool)
            ).view(1, 1, max_positions, max_positions),
            persistent=False,
        )
        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

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

        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._ntk_cached = 1.0

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

    def _attn(self, query, key, value, 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, 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 = self.bias[
            :, :, 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]],
        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)

        kv_seq_len = hidden_states.size()[1]
        if layer_past:
            # layer past[0] shape: bs * seq_len * head_num * dim
            kv_seq_len += layer_past[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)
            self._ntk_cached = ntk_alpha
        else:
            ntk_alpha = self._ntk_cached
        rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha).to(
            hidden_states.device
        )

        if rotary_pos_emb is not None:
            if isinstance(rotary_pos_emb, tuple):
                rotary_pos_emb = rotary_pos_emb
            else:
                rotary_pos_emb = (rotary_pos_emb,) * 2

        if rotary_pos_emb is not None:
            q_pos_emb, k_pos_emb = rotary_pos_emb
            # Slice the pos emb for current inference
            cur_len = query.shape[1]
            q_pos_emb = q_pos_emb[:, -cur_len:, :, :]
            k_pos_emb = k_pos_emb[:, -cur_len:, :, :]
            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, 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]],
        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,
            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.wte = nn.Embedding(self.vocab_size, self.embed_dim)

        self.drop = nn.Dropout(config.emb_dropout_prob)
        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 input_ids is not 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)
        else:
            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

        hidden_states = self.drop(hidden_states)
        if 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,
                    None,
                    attention_mask,
                    head_mask[i],
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    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[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[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] if v is not None
            )

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class QWenLMHeadModel(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.transformer = QWenModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        if config.bf16:
            self.transformer.bfloat16()
            self.lm_head.bfloat16()
        if config.fp16:
            self.transformer.half()
            self.lm_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,
        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]

        lm_logits = self.lm_head(hidden_states)

        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()
            loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
            )

        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,
            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,
        **kwargs,
    ) -> Tuple[str, HistoryType]:
        assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
        assert self.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 = self.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=self.generation_config.chat_format,
        )

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

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

        if append_history:
            history.append((query, response))

        return response, history

    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,
            **kwargs,
    ) -> Generator[str, Any, None]:
        assert self.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 = self.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=self.generation_config.chat_format,
        )

        stop_words_ids.extend(get_stop_words_ids(
            self.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=self.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(**self.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')

        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,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        # 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(self.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=self.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)

        return super().generate(
            inputs,
            generation_config=generation_config,
            logits_processor=logits_processor,
            stopping_criteria=stopping_criteria,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            synced_gpus=synced_gpus,
            assistant_model=assistant_model,
            streamer=streamer,
            **kwargs,
        )


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

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

    def forward(self, max_seq_len, offset=0, ntk_alpha=1.0):
        self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha)
        return self._rotary_pos_emb_cache[:, 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):
    if apply_rotary_emb_func is not None and t.is_cuda:
        t_ = t.float()
        freqs = freqs.squeeze(0).squeeze(1)
        cos = freqs[:, : freqs.shape[-1] // 2].cos()
        sin = freqs[:, : freqs.shape[-1] // 2].sin()
        output = apply_rotary_emb_func(t_, cos, sin).type_as(t)
        return output
    else:
        rot_dim = freqs.shape[-1]
        t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
        t_ = t_.float()
        t_pass_ = t_pass_.float()
        t_ = (t_ * freqs.cos()) + (_rotate_half(t_) * freqs.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