Update configuration_rwkv6qwen2.py

configuration_rwkv6qwen2.py

@@ -1,11 +1,6 @@
 # coding=utf-8
 # Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
 #
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -17,1174 +12,195 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""PyTorch RWKV6Qwen2 model."""
-
-import math
-import inspect
-from typing import List, Optional, Tuple, Union, Dict, Any
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-import torch.nn.functional as F
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+"""RWKV6Qwen2 model configuration"""
 
-from transformers.cache_utils import Cache, StaticCache, DynamicCache
-from transformers.generation import GenerationMixin
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_code_sample_docstrings,
-    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_rwkv6qwen2 import RWKV6Qwen2Config
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
 
-from transformers.models.qwen2.modeling_qwen2 import Qwen2DecoderLayer, Qwen2MLP, Qwen2RMSNorm, repeat_kv
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
 
 logger = logging.get_logger(__name__)
 
 
-_CHECKPOINT_FOR_DOC = "RWKV/RWKV6Qwen2-7B"
-_CONFIG_FOR_DOC = "RWKV6Qwen2Config"
-
-class RWKV6State():
-    def __init__(self) -> None:
-        super().__init__()
-        self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
-        self.layer_kv_states: List[torch.Tensor] = []
-        self.layer_shift_states:  List[torch.Tensor] = []
-
-    def __getitem__(self, layer_idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
-        sequence length.
-        """
-        if layer_idx < len(self):
-            return (self.layer_kv_states[layer_idx], self.layer_shift_states[layer_idx])
-        else:
-            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
-
-    def __iter__(self):
-        """
-        Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over
-        keys and values
-        """
-        for layer_idx in range(len(self)):
-            yield (self.layer_kv_states[layer_idx], self.layer_shift_states[layer_idx])
-
-    def __len__(self):
-        """
-        Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
-        to the number of layers in the model.
-        """
-        return len(self.layer_kv_states)
-
-    def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -> int:
-        """Given the sequence length of the new inputs, returns the usable length of the cache."""
-        # Linear Attention variants do not have a maximum length
-        return new_seq_length
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        raise NotImplementedError('Cannot reorder Linear Attention state')
-
-    def get_seq_length(self, layer_idx: int = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        return self._seen_tokens
-
-    def get_max_cache_shape(self) -> Optional[int]:
-        """Returns the maximum sequence length of the cache object. DynamicCache does not have a maximum length."""
-        return None
-
-    def get_max_length(self) -> Optional[int]:
-        """
-        Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length.
-        """
-        return None
-
-    def crop(self, max_length: int):
-        # can't implement this for linear attention variants
-        return
-
-    @torch.no_grad
-    def update(
-        self,
-        kv_state: torch.Tensor,
-        shift_state: torch.Tensor,
-        layer_idx: int,
-        token_count: int = 0,
-        cache_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:        
-        # Update the number of seen tokens
-        if layer_idx == 0:
-            self._seen_tokens += token_count
-
-        # Update the cache
-        # There may be skipped layers, fill them with empty lists
-        for _ in range(len(self.layer_kv_states), layer_idx + 1):
-            self.layer_kv_states.append(torch.zeros_like(kv_state).requires_grad_(False))
-            self.layer_shift_states.append(torch.zeros_like(shift_state).requires_grad_(False))
-        self.layer_kv_states[layer_idx].copy_(kv_state)
-        self.layer_shift_states[layer_idx].copy_(shift_state)
-
-        return self.layer_kv_states[layer_idx], self.layer_shift_states[layer_idx]
-
-try:
-    #from fla.ops.gla.chunk import chunk_gla
-    from fla.ops.gla.fused_recurrent import fused_recurrent_gla
-except ImportError:
-    print("Required module is not installed. Please install it using the following commands:")
-    print("pip install --no-use-pep517 flash-linear-attention")
-    print("Additionally, ensure you have at least version 2.2.0 of Triton installed:")
-    print("pip install triton>=2.2.0")
-
-class Qwen2RotaryEmbedding(nn.Module):
-    def __init__(self, config: RWKV6Qwen2Config, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and 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.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.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
-            # This .to() is needed if the model has been moved to a device after being initialized (because
-            # the buffer is automatically moved, but not the original copy)
-            self.original_inv_freq = self.original_inv_freq.to(device)
-            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)
+class RWKV6Qwen2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`RWKV6Qwen2Model`]. It is used to instantiate a
+    RWKV6Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
 
-        # 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()
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
 
-        # 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)
-    
-def generate_rotary_embedding(max_seqlen:int, dim:int, theta:float = 10000.0, scale:float = 1):
-    #inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float).to(device) / dim))
-
-    angular_velocity = theta ** -(torch.arange(0, dim, 2, dtype=torch.float) / dim) / scale # frequencies from 1.0 ... 1/theta
-    angles = torch.outer(torch.arange(max_seqlen), angular_velocity)
-    # Different from paper, but it uses a different permutation in order to obtain the same calculation
-    emb = torch.cat((angles, angles), dim=-1)
-    return torch.stack([emb.cos(), emb.sin()], dim=0)
-    #return torch.polar(torch.ones_like(angles), angles)
-
-# 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)
-
-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
-
-def ortho_init(x, scale):
-    with torch.no_grad():
-        shape = x.shape
-        if len(shape) == 2:
-            gain = math.sqrt(shape[0] / shape[1]) if shape[0] > shape[1] else 1
-            #nn.init.orthogonal_(x, gain=gain * scale)
-            x.copy_(nn.init.orthogonal_(torch.empty_like(x, dtype=torch.float32), gain=gain * scale))
-        elif len(shape) == 3:
-            gain = math.sqrt(shape[1] / shape[2]) if shape[1] > shape[2] else 1
-            for i in range(shape[0]):
-                #nn.init.orthogonal_(x[i], gain=gain * scale)
-                x[i].copy_(nn.init.orthogonal_(torch.empty_like(x[i], dtype=torch.float32), gain=gain * scale))                            
-        else:
-            assert False
-        return x
-
-class RWKV6Attention(nn.Module):
-    def __init__(self, config, 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 = getattr(config, '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.attention_dropout = config.attention_dropout
-
-        n_layer = self.config.num_hidden_layers
-        n_embd = self.hidden_size
-        dim_att = self.num_heads * self.head_dim
-        layer_id = self.layer_idx
-
-        if self.hidden_size % self.num_heads != 0:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=getattr(config, 'attention_output_bias', config.attention_bias))
-
-        calc_lora_rank = lambda exponent, multiplier: max(1, round(self.hidden_size ** exponent * multiplier / 32)) * 32
-
-        if config.gate_rank_type == 1:
-            self.gate = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        elif config.gate_rank_type == 2:
-            lora_rank_gate = config.lora_rank_gate or calc_lora_rank(0.8, 0.6)
-            self.g1 = nn.Parameter(torch.empty(n_embd, lora_rank_gate))
-            self.g2 = nn.Parameter(torch.empty(lora_rank_gate, n_embd))
-
-        if config.groupnorm_att:
-            self.ln_x = nn.GroupNorm(self.num_heads, dim_att, eps=self.head_dim * 1e-5)
-
-        with torch.no_grad():
-            if config.gate_rank_type == 1:
-                self.gate.weight.zero_()                
-            elif config.gate_rank_type == 2:
-                self.g1.zero_()
-                ortho_init(self.g2, 0.1)
-
-            ratio_0_to_1 = layer_id / (n_layer - 1)  # 0 to 1
-            ratio_1_to_almost0 = 1.0 - (layer_id / n_layer)  # 1 to ~0
-            
-            if self.config.use_tokenshift:
-                ddd = torch.ones(1, 1, n_embd)
-                for i in range(n_embd):
-                    ddd[0, 0, i] = i / n_embd
-
-                ddd = torch.zeros(1, 1, n_embd)
-                self.time_maa_x = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
-                self.time_maa_r = nn.Parameter(torch.zeros_like(ddd))
-                self.time_maa_k = nn.Parameter(torch.zeros_like(ddd))
-                self.time_maa_v = nn.Parameter(torch.zeros_like(ddd))
-                self.time_maa_w = nn.Parameter(torch.zeros_like(ddd))
-                self.time_maa_g = nn.Parameter(torch.zeros_like(ddd))
-
-                lora_rank_tokenshift = config.lora_rank_tokenshift or (32 if n_embd < 4096 else 64)
-
-                self.time_maa_w2 = nn.Parameter(torch.zeros(5, lora_rank_tokenshift, n_embd).uniform_(-0.01, 0.01))
-                self.time_maa_w1 = nn.Parameter(torch.zeros(n_embd, lora_rank_tokenshift*self.time_maa_w2.size(0)))
-
-            lora_rank_decay = config.lora_rank_decay or (64 if n_embd < 4096 else 128)
-
-            # RWKV-6
-            decay_speed = torch.ones(dim_att)
-            for n in range(dim_att):
-                decay_speed[n] = -6 + 5 * (n / (dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
-            self.time_decay = nn.Parameter(decay_speed.reshape(1,1,dim_att))
-            self.time_decay_w1 = nn.Parameter(torch.zeros(n_embd, lora_rank_decay))
-            self.time_decay_w2 = nn.Parameter(torch.zeros(lora_rank_decay, dim_att).uniform_(-0.01, 0.01))
-
-    def forward(
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the RWKV6Qwen2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`RWKV6Qwen2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import RWKV6Qwen2Model, RWKV6Qwen2Config
+
+    >>> # Initializing a RWKV6Qwen2 style configuration
+    >>> configuration = RWKV6Qwen2Config()
+
+    >>> # Initializing a model from the RWKV6Qwen2-7B style configuration
+    >>> model = RWKV6Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "rwkv6qwen2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
         self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[RWKV6State] = 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
-    ):
-        output_shift_state = hidden_states[:, -1:].detach().clone()
-
-        x = hidden_states
-
-        B, T, C = hidden_states.shape
-        H = self.num_heads
-        N = self.head_dim
-
-        if use_cache and past_key_values is not None and len(past_key_values) > self.layer_idx:
-            input_kv_state, input_shift_state = past_key_values[self.layer_idx]
-            xprev = torch.cat([input_shift_state, x[:, :-1]], dim=1)
-        else:
-            input_kv_state = None
-            xprev = F.pad(x, (0, 0, 1, -1))
-
-        if self.config.use_tokenshift:
-            dxprev = xprev - x
-
-            xxx = x + dxprev * self.time_maa_x
-            xxx = torch.tanh(xxx @ self.time_maa_w1).view(B*T, self.time_maa_w2.size(0), -1).transpose(0, 1)
-            xxx = torch.bmm(xxx, self.time_maa_w2).view(self.time_maa_w2.size(0), B, T, C)
-
-            mr, mk, mv, mw, mg = xxx.unbind(dim=0)
-            xr = x + dxprev * (self.time_maa_r + mr)
-            xk = x + dxprev * (self.time_maa_k + mk)
-            xv = x + dxprev * (self.time_maa_v + mv)
-            xw = x + dxprev * (self.time_maa_w + mw)
-            xg = x + dxprev * (self.time_maa_g + mg)
-        else:
-            xr = xk = xv = xw = xg = x
-
-        r = self.q_proj(xr)
-        k = self.k_proj(xk)
-        v = self.v_proj(xv)
-        w_lora_result = (self.time_decay + torch.tanh(xw @ self.time_decay_w1) @ self.time_decay_w2).to(r.dtype)
-        if self.config.gate_rank_type == 1:
-            g = torch.sigmoid(self.gate(xg))
-        elif self.config.gate_rank_type == 2:
-            g = torch.sigmoid(xg @ self.g1) @ self.g2
-
-        if position_embeddings is not None:
-            r = r.view(B,T,-1,N)
-            k = k.view(B,T,-1,N)
-            cos, sin = position_embeddings
-            r, k = apply_rotary_pos_emb(r, k, cos, sin, unsqueeze_dim=2)
-
-        # repeat k/v heads if n_kv_heads < n_heads
-        k = k.view(B, T, -1, 1, self.head_dim).expand(-1, -1, -1, self.num_key_value_groups, -1).reshape(B, T, -1)
-        v = v.view(B, T, -1, 1, self.head_dim).expand(-1, -1, -1, self.num_key_value_groups, -1).reshape(B, T, -1)
-        dropout_rate = 0.0 if not self.training else self.attention_dropout
-
-        log_w = -w_lora_result.float().exp()
-        log_w = log_w.clamp(-5)
-        if self.config.balance_state:
-            k = (k * (1 - log_w.exp())).to(k.dtype)
-
-        # dealing with left-padding
-        if attention_mask is not None:
-            v = v * attention_mask[:, -v.shape[-2]:, None]
-
-        r = r.view(B,T,-1,N).to(v.dtype)
-        k = k.view(B,T,-1,N).to(v.dtype)
-        v = v.view(B,T,-1,N)
-        log_w = log_w.view(B,T,-1,N)
-
-        attn_weights = torch.empty(0, device=x.device)
-
-        scale = r.shape[-1] ** -0.5
-        output_final_state = not self.training and use_cache and past_key_values is not None
-        attn_output, output_kv_state = fused_recurrent_gla(r, k, v, log_w, None, scale, input_kv_state, output_final_state)
-
-        attn_output = attn_output.view(B, T, -1)
-        if self.config.groupnorm_att:
-            attn_output = self.ln_x(attn_output.view(B * T, -1)).view(B, T, -1)
-        if self.config.gate_rank_type != 0:
-            attn_output = attn_output * g
-        attn_output = self.o_proj(attn_output)
-
-        if output_final_state:
-            past_key_values.update(output_kv_state, output_shift_state, self.layer_idx, T)
-
-        return attn_output, attn_weights
-    
-class RWKV6Qwen2DecoderLayer(Qwen2DecoderLayer):
-    def __init__(self, config: RWKV6Qwen2Config, layer_idx: int):
-        nn.Module.__init__(self)
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = RWKV6Attention(config, layer_idx) #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_values: Optional[Cache] = 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,  # necessary, but kept here for BC
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        lora_rank_tokenshift=None,
+        lora_rank_decay=None,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        use_rope=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        attention_bias=True,
+        attention_output_bias=False,
+        gate_rank_type=1,
+        lora_rank_gate=None,        
+        balance_state=True,
+        groupnorm_att=False,
+        use_tokenshift=True,
         **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if use_sliding_window else None
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.lora_rank_tokenshift = lora_rank_tokenshift
+        self.lora_rank_decay = lora_rank_decay
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.use_rope = use_rope
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        self.attention_bias = attention_bias
+        self.attention_output_bias = attention_output_bias
+        self.gate_rank_type = gate_rank_type
+        self.lora_rank_gate = lora_rank_gate        
+        self.balance_state = balance_state
+        self.groupnorm_att = groupnorm_att
+        self.use_tokenshift = use_tokenshift
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
             **kwargs,
-        )
-        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,)
-
-        return outputs
-
-RWKV6QWEN2_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 ([`RWKV6Qwen2Config`]):
-            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.",
-    RWKV6QWEN2_START_DOCSTRING,
-)
-class RWKV6Qwen2PreTrainedModel(PreTrainedModel):
-    config_class = RWKV6Qwen2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["RWKV6Qwen2DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = 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_()
-
-
-RWKV6QWEN2_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 RWKV6Qwen2 Model outputting raw hidden-states without any specific head on top.",
-    RWKV6QWEN2_START_DOCSTRING,
-)
-class RWKV6Qwen2Model(RWKV6Qwen2PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
-
-    Args:
-        config: RWKV6Qwen2Config
-    """
-
-    def __init__(self, config: RWKV6Qwen2Config):
-        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(
-            [RWKV6Qwen2DecoderLayer(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(RWKV6QWEN2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and not isinstance(past_key_values, RWKV6State):
-            past_key_values = RWKV6State()
-
-        #if cache_position is None:
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        cache_position = torch.arange(
-            past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-        )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        # causal_mask = self._update_causal_mask(
-        #     attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        # )
-
-        causal_mask = None
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        if self.config.use_rope:
-            position_embeddings = self.rotary_emb(hidden_states, position_ids)
-        else:
-            position_embeddings = None
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                    position_embeddings,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_values=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                    position_embeddings=position_embeddings,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        #if return_legacy_cache:
-        #    next_cache = next_cache.to_legacy_cache()
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-class RWKV6Qwen2ForCausalLM(RWKV6Qwen2PreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = RWKV6Qwen2Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(RWKV6QWEN2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        num_logits_to_keep: int = 0,
-        **loss_kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-            num_logits_to_keep (`int`, *optional*):
-                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
-                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, RWKV6Qwen2ForCausalLM
-
-        >>> model = RWKV6Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-
-        hidden_states = outputs[0]
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-@add_start_docstrings(
-    """
-    The RWKV6Qwen2 Model transformer with a sequence classification head on top (linear layer).
-
-    [`RWKV6Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    RWKV6QWEN2_START_DOCSTRING,
-)
-class RWKV6Qwen2ForSequenceClassification(RWKV6Qwen2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = RWKV6Qwen2Model(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(RWKV6QWEN2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The RWKV6Qwen2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
-    output) e.g. for Named-Entity-Recognition (NER) tasks.
-    """,
-    RWKV6QWEN2_START_DOCSTRING,
-)
-# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->RWKV6Qwen2, LLAMA->RWKV6QWEN2
-class RWKV6Qwen2ForTokenClassification(RWKV6Qwen2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = RWKV6Qwen2Model(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(RWKV6QWEN2_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-The RWKV6Qwen2 Model transformer with a span classification head on top for extractive question-answering tasks like
-SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    RWKV6QWEN2_START_DOCSTRING,
-)
-# Copied from transformers.models.mistral.modeling_mistral.MistralForQuestionAnswering with Mistral->RWKV6Qwen2, MISTRAL->RWKV6QWEN2
-class RWKV6Qwen2ForQuestionAnswering(RWKV6Qwen2PreTrainedModel):
-    base_model_prefix = "model"
-
-    # Copied from models.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->RWKV6Qwen2
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = RWKV6Qwen2Model(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, 2)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(RWKV6QWEN2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        **kwargs,
-    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        loss = None
-        if start_positions is not None and end_positions is not None:
-            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
         )