content: # Copyright 2024 The Qwen Team and The HuggingFace Inc. team.
  # SPDX-License-Identifier: Apache-2.0
  
  """PyTorch Qwen2 model."""
  
  import math
  from typing import List, Optional, Tuple, Union
  
  import torch
  import torch.utils.checkpoint
  from torch import nn
  
  from transformers.activations import ACT2FN
  from transformers.cache_utils import Cache, DynamicCache
  from transformers.generation import GenerationMixin
  from transformers.modeling_outputs import (
      BaseModelOutputWithPast,
      CausalLMOutputWithPast,
  )
  from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import (
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      is_flash_attn_2_available,
      is_flash_attn_greater_or_equal_2_10,
      logging,
      replace_return_docstrings,
  )
  from .configuration_qwen2 import Qwen2Config
  
  
  if is_flash_attn_2_available():
      from transformers.modeling_flash_attention_utils import _flash_attention_forward
  
  
  logger = logging.get_logger(__name__)
  
  
  _CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B"
  _CONFIG_FOR_DOC = "Qwen2Config"
  
  
  # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2
  class Qwen2RMSNorm(nn.Module):
      def __init__(self, hidden_size, eps=1e-6):
          """
          Qwen2RMSNorm is equivalent to T5LayerNorm
          """
          super().__init__()
          self.weight = nn.Parameter(torch.ones(hidden_size))
          self.variance_epsilon = eps
  
      def forward(self, hidden_states):
          input_dtype = hidden_states.dtype
          hidden_states = hidden_states.to(torch.float32)
          variance = hidden_states.pow(2).mean(-1, keepdim=True)
          hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
          return self.weight * hidden_states.to(input_dtype)
  
      def extra_repr(self):
          return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
  
  
  # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2
  class Qwen2RotaryEmbedding(nn.Module):
      def __init__(
          self,
          dim=None,
          max_position_embeddings=2048,
          base=10000,
          device=None,
          scaling_factor=1.0,
          rope_type="default",
          config: Optional[Qwen2Config] = None,
      ):
          super().__init__()
          # TODO (joao): remove the `if` below, only used for BC
          self.rope_kwargs = {}
          if config is None:
              logger.warning_once(
                  "`Qwen2RotaryEmbedding` can now be fully parameterized by passing the model config through the "
                  "`config` argument. All other arguments will be removed in v4.46"
              )
              self.rope_kwargs = {
                  "rope_type": rope_type,
                  "factor": scaling_factor,
                  "dim": dim,
                  "base": base,
                  "max_position_embeddings": max_position_embeddings,
              }
              self.rope_type = rope_type
              self.max_seq_len_cached = max_position_embeddings
              self.original_max_seq_len = max_position_embeddings
          else:
              # BC: "rope_type" was originally "type"
              if config.rope_scaling is not None:
                  self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
              else:
                  self.rope_type = "default"
              self.max_seq_len_cached = config.max_position_embeddings
              self.original_max_seq_len = config.max_position_embeddings
  
          self.config = config
          self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
  
          inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
          self.register_buffer("inv_freq", inv_freq, persistent=False)
          self.original_inv_freq = self.inv_freq
  
      def _dynamic_frequency_update(self, position_ids, device):
          """
          dynamic RoPE layers should recompute `inv_freq` in the following situations:
          1 - growing beyond the cached sequence length (allow scaling)
          2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
          """
          seq_len = torch.max(position_ids) + 1
          if seq_len > self.max_seq_len_cached:  # growth
              inv_freq, self.attention_scaling = self.rope_init_fn(
                  self.config, device, seq_len=seq_len, **self.rope_kwargs
              )
              self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
              self.max_seq_len_cached = seq_len
  
          if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
              self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
              self.max_seq_len_cached = self.original_max_seq_len
  
      @torch.no_grad()
      def forward(self, x, position_ids):
          if "dynamic" in self.rope_type:
              self._dynamic_frequency_update(position_ids, device=x.device)
  
          # Core RoPE block
          inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
          position_ids_expanded = position_ids[:, None, :].float()
          # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
          device_type = x.device.type
          device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
          with torch.autocast(device_type=device_type, enabled=False):
              freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
              emb = torch.cat((freqs, freqs), dim=-1)
              cos = emb.cos()
              sin = emb.sin()
  
          # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
          cos = cos * self.attention_scaling
          sin = sin * self.attention_scaling
  
          return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
  
  
  # Copied from transformers.models.llama.modeling_llama.rotate_half
  def rotate_half(x):
      """Rotates half the hidden dims of the input."""
      x1 = x[..., : x.shape[-1] // 2]
      x2 = x[..., x.shape[-1] // 2 :]
      return torch.cat((-x2, x1), dim=-1)
  
  
  # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
  def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
      """Applies Rotary Position Embedding to the query and key tensors.
  
      Args:
          q (`torch.Tensor`): The query tensor.
          k (`torch.Tensor`): The key tensor.
          cos (`torch.Tensor`): The cosine part of the rotary embedding.
          sin (`torch.Tensor`): The sine part of the rotary embedding.
          position_ids (`torch.Tensor`, *optional*):
              Deprecated and unused.
          unsqueeze_dim (`int`, *optional*, defaults to 1):
              The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
              sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
              that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
              k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
              cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
              the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
      Returns:
          `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
      """
      cos = cos.unsqueeze(unsqueeze_dim)
      sin = sin.unsqueeze(unsqueeze_dim)
      q_embed = (q * cos) + (rotate_half(q) * sin)
      k_embed = (k * cos) + (rotate_half(k) * sin)
      return q_embed, k_embed
  
  
  # Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2
  class Qwen2MLP(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.hidden_size = config.hidden_size
          self.intermediate_size = config.intermediate_size
          self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
          self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
          self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
          self.act_fn = ACT2FN[config.hidden_act]
  
      def forward(self, hidden_state):
          return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
  
  
  # Copied from transformers.models.llama.modeling_llama.repeat_kv
  def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
      """
      This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
      num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
      """
      batch, num_key_value_heads, slen, head_dim = hidden_states.shape
      if n_rep == 1:
          return hidden_states
      hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
      return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
  
  
  class Qwen2Attention(nn.Module):
      """
      Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
      and "Generating Long Sequences with Sparse Transformers".
      """
  
      def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None):
          super().__init__()
          self.config = config
          self.layer_idx = layer_idx
          if layer_idx is None:
              logger.warning_once(
                  f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
                  "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                  "when creating this class."
              )
  
          self.hidden_size = config.hidden_size
          self.num_heads = config.num_attention_heads
          self.head_dim = self.hidden_size // self.num_heads
          self.num_key_value_heads = config.num_key_value_heads
          self.num_key_value_groups = self.num_heads // self.num_key_value_heads
          self.max_position_embeddings = config.max_position_embeddings
          self.rope_theta = config.rope_theta
          self.is_causal = config.is_causal
          self.attention_dropout = config.attention_dropout
  
          if (self.head_dim * self.num_heads) != self.hidden_size:
              raise ValueError(
                  f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                  f" and `num_heads`: {self.num_heads})."
              )
          self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
          self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
          self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
          self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          position_ids: Optional[torch.LongTensor] = None,
          past_key_value: Optional[Cache] = None,
          output_attentions: bool = False,
          use_cache: bool = False,
          cache_position: Optional[torch.LongTensor] = None,
          position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
          bsz, q_len, _ = hidden_states.size()
  
          query_states = self.q_proj(hidden_states)
          key_states = self.k_proj(hidden_states)
          value_states = self.v_proj(hidden_states)
  
          query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
          key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
          value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
  
          if position_embeddings is None:
              logger.warning_once(
                  "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
                  "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
                  "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
                  "removed and `position_embeddings` will be mandatory."
              )
              cos, sin = self.rotary_emb(value_states, position_ids)
          else:
              cos, sin = position_embeddings
          query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
  
          if past_key_value is not None:
              cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
              key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
  
          # repeat k/v heads if n_kv_heads < n_heads
          key_states = repeat_kv(key_states, self.num_key_value_groups)
          value_states = repeat_kv(value_states, self.num_key_value_groups)
  
          attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
          if attention_mask is not None:  # no matter the length, we just slice it
              causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
              attn_weights = attn_weights + causal_mask
  
          # upcast attention to fp32
          attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
          attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
          attn_output = torch.matmul(attn_weights, value_states)
  
          if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
              raise ValueError(
                  f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                  f" {attn_output.size()}"
              )
  
          attn_output = attn_output.transpose(1, 2).contiguous()
          attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
  
          attn_output = self.o_proj(attn_output)
  
          if not output_attentions:
              attn_weights = None
  
          return attn_output, attn_weights, past_key_value
  
  
  class Qwen2FlashAttention2(Qwen2Attention):
      """
      Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention`
      as the weights of the module stays untouched. The only required change would be on the forward pass
      where it needs to correctly call the public API of flash attention and deal with padding tokens
      in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
      config.max_window_layers layers.
      """
  
      # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
          # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
          # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
          self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          position_ids: Optional[torch.LongTensor] = None,
          past_key_value: Optional[Cache] = None,
          output_attentions: bool = False,
          use_cache: bool = False,
          cache_position: Optional[torch.LongTensor] = None,
          position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
      ):
          bsz, q_len, _ = hidden_states.size()
  
          query_states = self.q_proj(hidden_states)
          key_states = self.k_proj(hidden_states)
          value_states = self.v_proj(hidden_states)
  
          query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
          key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
          value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
  
          if position_embeddings is None:
              logger.warning_once(
                  "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
                  "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
                  "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
                  "removed and `position_embeddings` will be mandatory."
              )
              cos, sin = self.rotary_emb(value_states, position_ids)
          else:
              cos, sin = position_embeddings
          query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
  
          if past_key_value is not None:
              cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
              key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
  
          # repeat k/v heads if n_kv_heads < n_heads
          key_states = repeat_kv(key_states, self.num_key_value_groups)
          value_states = repeat_kv(value_states, self.num_key_value_groups)
          dropout_rate = 0.0 if not self.training else self.attention_dropout
  
          # In PEFT, usually we cast the layer norms in float32 for training stability reasons
          # therefore the input hidden states gets silently casted in float32. Hence, we need
          # cast them back in float16 just to be sure everything works as expected.
          input_dtype = query_states.dtype
          if input_dtype == torch.float32:
              if torch.is_autocast_enabled():
                  target_dtype = torch.get_autocast_gpu_dtype()
              # Handle the case where the model is quantized
              elif hasattr(self.config, "_pre_quantization_dtype"):
                  target_dtype = self.config._pre_quantization_dtype
              else:
                  target_dtype = self.q_proj.weight.dtype
  
              logger.warning_once(
                  f"The input hidden states seems to be silently casted in float32, this might be related to"
                  f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                  f" {target_dtype}."
              )
  
              query_states = query_states.to(target_dtype)
              key_states = key_states.to(target_dtype)
              value_states = value_states.to(target_dtype)
  
          # Reashape to the expected shape for Flash Attention
          query_states = query_states.transpose(1, 2)
          key_states = key_states.transpose(1, 2)
          value_states = value_states.transpose(1, 2)
  
          if (
              self.config.use_sliding_window
              and getattr(self.config, "sliding_window", None) is not None
              and self.layer_idx >= self.config.max_window_layers
          ):
              sliding_window = self.config.sliding_window
          else:
              sliding_window = None
  
          attn_output = _flash_attention_forward(
              query_states,
              key_states,
              value_states,
              attention_mask,
              q_len,
              position_ids=position_ids,
              dropout=dropout_rate,
              sliding_window=sliding_window,
              is_causal=self.is_causal,
              use_top_left_mask=self._flash_attn_uses_top_left_mask,
          )
  
          attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
          attn_output = self.o_proj(attn_output)
  
          if not output_attentions:
              attn_weights = None
  
          return attn_output, attn_weights, past_key_value
  
  
  QWEN2_ATTENTION_CLASSES = {
      "eager": Qwen2Attention,
      "flash_attention_2": Qwen2FlashAttention2,
  }
  
  
  class Qwen2DecoderLayer(nn.Module):
      def __init__(self, config: Qwen2Config, layer_idx: int):
          super().__init__()
          self.hidden_size = config.hidden_size
  
          if config.sliding_window and config._attn_implementation != "flash_attention_2":
              logger.warning_once(
                  f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
                  "unexpected results may be encountered."
              )
          self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
  
          self.mlp = Qwen2MLP(config)
          self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
          self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          position_ids: Optional[torch.LongTensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          output_attentions: Optional[bool] = False,
          use_cache: Optional[bool] = False,
          cache_position: Optional[torch.LongTensor] = None,
          position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
          **kwargs,
      ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
          """
          Args:
              hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
              attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                  `(batch, sequence_length)` where padding elements are indicated by 0.
              output_attentions (`bool`, *optional*):
                  Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                  returned tensors for more detail.
              use_cache (`bool`, *optional*):
                  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                  (see `past_key_values`).
              past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
              cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                  Indices depicting the position of the input sequence tokens in the sequence.
              position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                  Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                  with `head_dim` being the embedding dimension of each attention head.
              kwargs (`dict`, *optional*):
                  Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
                  into the model
          """
  
          residual = hidden_states
  
          hidden_states = self.input_layernorm(hidden_states)
  
          # Self Attention
          hidden_states, self_attn_weights, present_key_value = self.self_attn(
              hidden_states=hidden_states,
              attention_mask=attention_mask,
              position_ids=position_ids,
              past_key_value=past_key_value,
              output_attentions=output_attentions,
              use_cache=use_cache,
              cache_position=cache_position,
              position_embeddings=position_embeddings,
          )
          hidden_states = residual + hidden_states
  
          # Fully Connected
          residual = hidden_states
          hidden_states = self.post_attention_layernorm(hidden_states)
          hidden_states = self.mlp(hidden_states)
          hidden_states = residual + hidden_states
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (self_attn_weights,)
  
          if use_cache:
              outputs += (present_key_value,)
  
          return outputs
  
  
  QWEN2_START_DOCSTRING = r"""
      This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
      Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
      and behavior.
  
      Parameters:
          config ([`Qwen2Config`]):
              Model configuration class with all the parameters of the model. Initializing with a config file does not
              load the weights associated with the model, only the configuration. Check out the
              [`~PreTrainedModel.from_pretrained`] method to load the model weights.
  """
  
  
  @add_start_docstrings(
      "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
      QWEN2_START_DOCSTRING,
  )
  class Qwen2PreTrainedModel(PreTrainedModel):
      config_class = Qwen2Config
      base_model_prefix = "model"
      supports_gradient_checkpointing = True
      _no_split_modules = ["Qwen2DecoderLayer"]
      _skip_keys_device_placement = "past_key_values"
      _supports_flash_attn_2 = True
      _supports_cache_class = True
      _supports_quantized_cache = True
      _supports_static_cache = True
  
      def _init_weights(self, module):
          std = self.config.initializer_range
          if isinstance(module, nn.Linear):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
  
  
  QWEN2_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
              it.
  
              Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
  
              Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
              `past_key_values`).
  
              If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
              and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
              information on the default strategy.
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
          position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
              config.n_positions - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
              Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
              blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
              returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
  
              Two formats are allowed:
              - a [`~cache_utils.Cache`] instance, see our
              [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
              - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
              shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
              cache format.
  
              The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
              legacy cache format will be returned.
  
              If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
              have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
              of shape `(batch_size, sequence_length)`.
          inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
              model's internal embedding lookup matrix.
          use_cache (`bool`, *optional*):
              If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
              `past_key_values`).
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
          cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
              Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
              this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
              the complete sequence length.
  """
  
  
  @add_start_docstrings(
      "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
      QWEN2_START_DOCSTRING,
  )
  class Qwen2Model(Qwen2PreTrainedModel):
      """
      Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
  
      Args:
          config: Qwen2Config
      """
  
      def __init__(self, config: Qwen2Config):
          super().__init__(config)
          self.padding_idx = config.pad_token_id
          self.vocab_size = config.vocab_size
  
          self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
          self.layers = nn.ModuleList(
              [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
          )
          self._attn_implementation = config._attn_implementation
          self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
          self.rotary_emb = Qwen2RotaryEmbedding(config=config)
  
          self.gradient_checkpointing = False
          # Initialize weights and apply final processing
          self.post_init()
  
      def get_input_embeddings(self):
          return self.embed_tokens
  
      def set_input_embeddings(self, value):
          self.embed_tokens = value
  
      @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids: torch.LongTensor = None,
          attention_mask: Optional[torch.Tensor] = None,
          position_ids: Optional[torch.LongTensor] = None,
  