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config.json

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+{
+  "architectures": [
+    "RWKV7Qwen2ForCausalLM"
+  ],
+  "auto_map": {
+      "AutoConfig": "configuration_rwkv7qwen2.RWKV7Qwen2Config",
+      "AutoModelForCausalLM": "modeling_rwkv7qwen2.RWKV7Qwen2ForCausalLM"
+  },
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "attention_output_bias": false,
+  "balance_state": false,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "groupnorm_att": true,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "rwkv7qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.1",
+  "use_cache": true,
+  "use_rope": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

configuration_rwkv7qwen2.py

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+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""RWKV7Qwen2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class RWKV7Qwen2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`RWKV7Qwen2Model`]. It is used to instantiate a
+    RWKV7Qwen2 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).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the RWKV7Qwen2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`RWKV7Qwen2Model`]
+        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`.
+        lora_rank_decay (`int`, *optional*):
+            The rank of the lora used to generate decay.
+        lora_rank_iclr (`int`, *optional*):
+            The rank of the lora used to generate the in-context learning rate.
+        lora_rank_value_residual_mix (`int`, *optional*):
+            The rank of the lora used to generate the value residual mix amount.
+        lora_rank_value_gate (`int`, *optional*):
+            The rank of the lora used to generate the gate.
+        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 RWKV7Qwen2Model, RWKV7Qwen2Config
+
+    >>> # Initializing a RWKV7Qwen2 style configuration
+    >>> configuration = RWKV7Qwen2Config()
+
+    >>> # Initializing a model from the RWKV7Qwen2-7B style configuration
+    >>> model = RWKV7Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "rwkv7qwen2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        lora_rank_decay=None,
+        lora_rank_iclr=None,
+        lora_rank_value_residual_mix=None,
+        lora_rank_gate=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,
+        num_attention_layers=0,
+        attention_dropout=0.0,
+        attention_bias=True,
+        attention_output_bias=False,
+        gate_rank_type=2,
+        balance_state=True,
+        groupnorm_att=False,
+        use_tokenshift=False,
+        **kwargs,
+    ):
+        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
+        self.num_attention_layers = num_attention_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_decay = lora_rank_decay
+        self.lora_rank_iclr = lora_rank_iclr
+        self.lora_rank_value_residual_mix = lora_rank_value_residual_mix
+        self.lora_rank_gate = lora_rank_gate
+        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.balance_state = balance_state
+        self.groupnorm_att = groupnorm_att
+        self.use_tokenshift = use_tokenshift
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.37.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:00807def558a587a079252582f13909cc7ff1172284109f1b31c358cc84ea49b
+size 15502671760

modeling_rwkv7qwen2.py

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+# 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch RWKV7Qwen2 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
+
+from transformers.cache_utils import Cache, StaticCache
+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_rwkv7qwen2 import RWKV7Qwen2Config
+
+from transformers.models.qwen2.modeling_qwen2 import Qwen2DecoderLayer, Qwen2MLP, Qwen2RMSNorm, Qwen2Attention
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "recursal/QRWKV7-7B-Instruct-Preview-v0.1"
+_CONFIG_FOR_DOC = "RWKV7Qwen2Config"
+
+class RWKV7State(Cache):
+    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 to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
+    #     """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
+    #     backward compatibility."""
+    #     legacy_cache = ()
+    #     for layer_idx in range(len(self)):
+    #         legacy_cache += ((self.layer_kv_states[layer_idx], self.layer_shift_states[layer_idx]),)
+    #     return legacy_cache
+
+    # @classmethod
+    # #@deprecate_kwarg("num_hidden_layers", version="4.47.0")
+    # def from_legacy_cache(
+    #     cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor, torch.FloatTensor]]] = None, num_hidden_layers: int | None = None
+    # ) -> "RWKV7State":
+    #     """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
+    #     backward compatibility."""
+    #     cache = cls()
+    #     if past_key_values is not None:
+    #         for layer_idx in range(len(past_key_values)):
+    #             layer_kv_state, layer_shift_state = past_key_values[layer_idx]
+    #             cache.update(layer_kv_state, layer_shift_state, layer_idx)
+    #     return cache
+
+    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,
+        token_count: int,
+        layer_idx: int,
+        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]
+
+    # @deprecate_kwarg("num_hidden_layers", version="4.47.0")
+    # def batch_split(
+    #     self, full_batch_size: int, split_size: int, num_hidden_layers: int = None
+    # ) -> List["DynamicCache"]:
+    #     """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+    #     `_split_model_inputs()` in `generation.utils`"""
+    #     out = []
+    #     for i in range(0, full_batch_size, split_size):
+    #         current_split = DynamicCache()
+    #         current_split._seen_tokens = self._seen_tokens
+    #         current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+    #         current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+    #         out.append(current_split)
+    #     return out
+
+    # @classmethod
+    # @deprecate_kwarg("num_hidden_layers", version="4.47.0")
+    # def from_batch_splits(cls, splits: List["DynamicCache"], num_hidden_layers: int = None) -> "DynamicCache":
+    #     """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+    #     `generation.utils`"""
+    #     cache = cls()
+    #     for idx in range(len(splits[0])):
+    #         key_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
+    #         value_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
+    #         if key_cache != []:
+    #             layer_keys = torch.cat(key_cache, dim=0)
+    #             layer_values = torch.cat(value_cache, dim=0)
+    #             cache.update(layer_keys, layer_values, idx)
+    #     return cache
+
+    # def batch_repeat_interleave(self, repeats: int):
+    #     """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+    #     for layer_idx in range(len(self)):
+    #         self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+    #         self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    # def batch_select_indices(self, indices: torch.Tensor):
+    #     """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+    #     for layer_idx in range(len(self)):
+    #         self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+    #         self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+
+try:
+    from fla.ops.rwkv7.chunk import chunk_rwkv7
+    from fla.ops.rwkv7.fused_recurrent import fused_recurrent_rwkv7
+except ImportError:
+    print("Required module is not installed. Please install it using the following commands:")
+    print("pip install -U git+https://github.com/fla-org/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: RWKV7Qwen2Config, 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)
+
+        # 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)
+    
+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)
+
+# # Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
+# def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim:int=1):
+#     B, L = q.size(0), q.size(-2)
+#     cos = cos[:L].unsqueeze(0).expand(B,L,-1).unsqueeze(unsqueeze_dim)
+#     sin = sin[:L].unsqueeze(0).expand(B,L,-1).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 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
+
+class RWKV7Attention(nn.Module):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        C = self.hidden_size = config.hidden_size
+        H = self.num_heads = config.num_attention_heads
+        N = 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
+
+        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
+        lora_rank_decay = config.lora_rank_decay or calc_lora_rank(0.5, 1.8)
+        lora_rank_iclr = config.lora_rank_iclr or calc_lora_rank(0.5, 1.8)
+        lora_rank_value_residual_mix = config.lora_rank_value_residual_mix or calc_lora_rank(0.5, 1.3)
+        lora_rank_gate = config.lora_rank_gate or calc_lora_rank(0.8, 0.6)
+
+        # self.x_r = nn.Parameter(torch.empty(1,1,C))
+        # self.x_w = nn.Parameter(torch.empty(1,1,C))
+        # self.x_k = nn.Parameter(torch.empty(1,1,C))
+        # self.x_v = nn.Parameter(torch.empty(1,1,C))
+        # self.x_a = nn.Parameter(torch.empty(1,1,C))
+        # self.x_g = nn.Parameter(torch.empty(1,1,C))
+
+        self.w0 = nn.Parameter(torch.empty(1,1,C))
+        self.w1 = nn.Parameter(torch.empty(C, lora_rank_decay))
+        self.w2 = nn.Parameter(torch.empty(lora_rank_decay, C))
+
+        self.a0 = nn.Parameter(torch.empty(1,1,C))
+        self.a1 = nn.Parameter(torch.empty(C, lora_rank_iclr))
+        self.a2 = nn.Parameter(torch.empty(lora_rank_iclr, C))
+
+        #if layer_idx > 0:
+        self.v0 = nn.Parameter(torch.empty(1,1,C))
+        self.v1 = nn.Parameter(torch.empty(C, lora_rank_value_residual_mix))
+        self.v2 = nn.Parameter(torch.empty(lora_rank_value_residual_mix, C))
+
+        if config.gate_rank_type == 1:
+            self.gate = nn.Linear(C, C, bias=False)
+        elif config.gate_rank_type == 2:
+            self.g1 = nn.Parameter(torch.empty(C, lora_rank_gate))
+            self.g2 = nn.Parameter(torch.empty(lora_rank_gate, C))
+
+        self.k_k = nn.Parameter(torch.empty(1,1,C))
+        self.k_a = nn.Parameter(torch.empty(1,1,C))
+        self.r_k = nn.Parameter(torch.empty(H,N))
+
+        if self.config.groupnorm_att:
+            self.ln_x = nn.GroupNorm(H, C, eps=self.head_dim * 1e-5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        v_first: Optional[torch.Tensor] = None, 
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[RWKV7State] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ):
+        if attention_mask is not None:
+            assert len(attention_mask.shape) == 2, (
+                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
+                "for padding purposes (0 indicating padding). "
+                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
+            )
+        
+        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
+        q_len = T
+
+        if use_cache and past_key_values is not None and len(past_key_values) > self.layer_idx:
+            input_vk_state, input_shift_state = past_key_values[self.layer_idx]
+        else:
+            input_vk_state, input_shift_state = torch.zeros(B,H,N,N, dtype=torch.float32,device=x.device), torch.zeros_like(x[:, -1:])
+
+        # NOTE - no need for this without tokenshift
+        # if attention_mask is not None:
+        #     hidden_states = hidden_states.mul(attention_mask[:, -hidden_states.shape[-2]:, None])
+
+        # shifted = torch.cat([input_shift_state, x[:, :-1]], dim=1)
+        # xx = shifted - x
+
+        # xr = x+xx*self.x_r
+        # xw = x+xx*self.x_w
+        # xk = x+xx*self.x_k
+        # xv = x+xx*self.x_v
+        # xa = x+xx*self.x_a
+        # xg = x+xx*self.x_g
+
+        xr = xw = xk = xv = xa = xg = x
+
+        r = self.q_proj(xr)
+        w_lora_result = self.w0 + (torch.tanh(xw @ self.w1) @ self.w2).float()
+        k = self.k_proj(xk)
+        v = self.v_proj(xv)
+        a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2)
+        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)
+            # r = r.transpose(1,2) # BHTN
+            # k = k.transpose(1,2) # B(kvh)TN
+            cos, sin = position_embeddings
+            # cos, sin = shared.angles.unbind(0)
+            r, k = apply_rotary_pos_emb(r, k, cos, sin, unsqueeze_dim=2)
+            # r = r.transpose(1,2).view(B,T,-1).to(v.dtype)
+            # k = k.transpose(1,2).view(B,T,-1).to(v.dtype)
+
+        # 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
+
+        if self.config.balance_state:
+            kk = torch.nn.functional.normalize(k.view(B,T,H,-1), dim=-1, p=2.0).view(B,T,-1)
+        else:
+            kk = torch.nn.functional.normalize((k * self.k_k).view(B,T,H,-1), dim=-1, p=2.0).view(B,T,-1)
+            k = k * (1 + (a-1) * self.k_a)
+        if self.layer_idx == 0: v_first = v
+        else: v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2)        
+
+        # dealing with left-padding
+        if attention_mask is not None:
+            v = v * attention_mask[:, -v.shape[-2]:, None]
+            
+        # if T == 1 or not self.training:
+        #     w = torch.exp(-0.606531 * torch.sigmoid(w_lora_result)) # 0.606531 = exp(-0.5)
+        #     output_vk_state = input_vk_state
+        #     for t in range(T):
+        #         r_, w_, k_, v_, kk_, a_ = r[:,t], w[:,t], k[:,t], v[:,t], kk[:,t], a[:,t]
+        #         vk = v_.view(B,H,N,1) @ k_.view(B,H,1,N)
+        #         ab = (-kk_).view(B,H,N,1) @ (kk_*a_).view(B,H,1,N)
+        #         output_vk_state = output_vk_state * w_.view(B,H,1,N) + output_vk_state @ ab.float() + vk.float()
+        #         x[:,t] = (output_vk_state.to(dtype=x.dtype) @ r_.view(B,H,N,1)).view(B,H*N)
+        #     # FIXME - support fast triton kernel for non-training pre-fill with state in and out
+        # else:
+        # FIXME - can simplify to 
+        # log_w = -math.exp(-0.5) * torch.sigmoid(w_lora_result.float())
+        log_neglog_w = - 0.5 - torch.nn.functional.softplus(-w_lora_result)
+        log_w = -log_neglog_w.float().exp()
+
+        if self.config.balance_state:
+            w = log_w.exp()
+            k = k * (1-w+a)
+
+        r,log_w,k,v,kk,a = [i.view(B,T,self.num_heads,-1) for i in [r,log_w,k,v,kk,a]]
+        if self.training:
+            x, output_vk_state = chunk_rwkv7(r, log_w, k, v, -kk, kk*a, initial_state=input_vk_state, output_final_state=use_cache)
+        else:
+            x, output_vk_state = fused_recurrent_rwkv7(r, log_w, k, v, -kk, kk*a, initial_state=input_vk_state, output_final_state=use_cache)
+
+        if self.config.groupnorm_att:
+            x = torch.nn.functional.group_norm(x.view(B*T,H*N).float(), num_groups=H, weight=self.ln_x.weight.float(), bias=self.ln_x.bias.float(), eps = self.ln_x.eps).view(B,T,H*N).to(x.dtype)
+        else:
+            x = x.view(B,T,H*N).to(x.dtype) * N ** -0.5
+        # x = x + ((r.view(B,T,H,-1)*k.view(B,T,H,-1)*self.r_k).sum(dim=-1, keepdim=True) * v.view(B,T,H,-1)).view(B,T,C)
+        x = self.o_proj(x * g)
+
+        output_final_state = not self.training and use_cache and past_key_values is not None
+        if output_final_state:
+            past_key_values.update(output_vk_state, output_shift_state, q_len, self.layer_idx)
+
+        return x, v_first
+    
+class RWKV7Qwen2DecoderLayer(nn.Module):
+    def __init__(self, config: RWKV7Qwen2Config, layer_idx: int):
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+
+        if layer_idx >= config.num_hidden_layers - config.num_attention_layers:
+            self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx)
+        else:
+            self.self_attn = RWKV7Attention(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,
+        v_first: Optional[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,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, v_first = self.self_attn(
+            hidden_states=hidden_states,
+            v_first=v_first,
+            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 = 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, v_first,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+    
+
+RWKV7QWEN2_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 ([`RWKV7Qwen2Config`]):
+            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.",
+    RWKV7QWEN2_START_DOCSTRING,
+)
+class RWKV7Qwen2PreTrainedModel(PreTrainedModel):
+    config_class = RWKV7Qwen2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RWKV7Qwen2DecoderLayer"]
+    _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_()
+
+
+RWKV7QWEN2_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 RWKV7Qwen2 Model outputting raw hidden-states without any specific head on top.",
+    RWKV7QWEN2_START_DOCSTRING,
+)
+class RWKV7Qwen2Model(RWKV7Qwen2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
+
+    Args:
+        config: RWKV7Qwen2Config
+    """
+
+    def __init__(self, config: RWKV7Qwen2Config):
+        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(
+            [RWKV7Qwen2DecoderLayer(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(RWKV7QWEN2_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:
+            if 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, RWKV7State):
+            past_key_values = RWKV7State()
+
+        #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
+        v_first = 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,
+                    v_first,
+                )
+            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,
+                    v_first=v_first,
+                )
+
+            hidden_states = layer_outputs[0]
+            v_first = layer_outputs[1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[2],)
+
+        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 RWKV7Qwen2ForCausalLM(RWKV7Qwen2PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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, RWKV7Qwen2ForCausalLM
+
+        >>> model = RWKV7Qwen2ForCausalLM.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,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[Cache] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        # only last token for `inputs_ids` if the `past_key_values` is not empty.
+        if past_key_values is not None and len(past_key_values) > 0:
+            input_ids = input_ids[:, -1:]
+
+        model_inputs = {
+            'past_key_values': past_key_values,
+            'attention_mask': attention_mask,
+            'cache_position': cache_position,
+        }
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs['inputs_embeds'] = inputs_embeds
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs['input_ids'] = input_ids.contiguous()
+
+        model_inputs.update(**kwargs)
+
+        # 8. Remove unexpected `generate` inputs (TODO @joao: fix trainer and examples)
+        model_inputs.pop("labels", None)
+
+        return model_inputs        
+
+@add_start_docstrings(
+    """
+    The RWKV7Qwen2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`RWKV7Qwen2ForSequenceClassification`] 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).
+    """,
+    RWKV7QWEN2_START_DOCSTRING,
+)
+class RWKV7Qwen2ForSequenceClassification(RWKV7Qwen2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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 RWKV7Qwen2 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.
+    """,
+    RWKV7QWEN2_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->RWKV7Qwen2, LLAMA->RWKV7QWEN2
+class RWKV7Qwen2ForTokenClassification(RWKV7Qwen2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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 RWKV7Qwen2 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`).
+    """,
+    RWKV7QWEN2_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralForQuestionAnswering with Mistral->RWKV7Qwen2, MISTRAL->RWKV7QWEN2
+class RWKV7Qwen2ForQuestionAnswering(RWKV7Qwen2PreTrainedModel):
+    base_model_prefix = "model"
+
+    # Copied from models.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->RWKV7Qwen2
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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,
+        )

modeling_rwkv7qwen2.py.bak

@@ -0,0 +1,1319 @@
+# 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch RWKV7Qwen2 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
+
+from transformers.cache_utils import Cache, StaticCache
+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_rwkv7qwen2 import RWKV7Qwen2Config
+
+from transformers.models.qwen2.modeling_qwen2 import Qwen2DecoderLayer, Qwen2MLP, Qwen2RMSNorm, Qwen2Attention
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "recursal/QRWKV7-7B-Instruct-Preview-v0.1"
+_CONFIG_FOR_DOC = "RWKV7Qwen2Config"
+
+class RWKV7State(Cache):
+    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 to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
+    #     """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
+    #     backward compatibility."""
+    #     legacy_cache = ()
+    #     for layer_idx in range(len(self)):
+    #         legacy_cache += ((self.layer_kv_states[layer_idx], self.layer_shift_states[layer_idx]),)
+    #     return legacy_cache
+
+    # @classmethod
+    # #@deprecate_kwarg("num_hidden_layers", version="4.47.0")
+    # def from_legacy_cache(
+    #     cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor, torch.FloatTensor]]] = None, num_hidden_layers: int | None = None
+    # ) -> "RWKV7State":
+    #     """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
+    #     backward compatibility."""
+    #     cache = cls()
+    #     if past_key_values is not None:
+    #         for layer_idx in range(len(past_key_values)):
+    #             layer_kv_state, layer_shift_state = past_key_values[layer_idx]
+    #             cache.update(layer_kv_state, layer_shift_state, layer_idx)
+    #     return cache
+
+    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,
+        token_count: int,
+        layer_idx: int,
+        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]
+
+    # @deprecate_kwarg("num_hidden_layers", version="4.47.0")
+    # def batch_split(
+    #     self, full_batch_size: int, split_size: int, num_hidden_layers: int = None
+    # ) -> List["DynamicCache"]:
+    #     """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+    #     `_split_model_inputs()` in `generation.utils`"""
+    #     out = []
+    #     for i in range(0, full_batch_size, split_size):
+    #         current_split = DynamicCache()
+    #         current_split._seen_tokens = self._seen_tokens
+    #         current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+    #         current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+    #         out.append(current_split)
+    #     return out
+
+    # @classmethod
+    # @deprecate_kwarg("num_hidden_layers", version="4.47.0")
+    # def from_batch_splits(cls, splits: List["DynamicCache"], num_hidden_layers: int = None) -> "DynamicCache":
+    #     """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+    #     `generation.utils`"""
+    #     cache = cls()
+    #     for idx in range(len(splits[0])):
+    #         key_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
+    #         value_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
+    #         if key_cache != []:
+    #             layer_keys = torch.cat(key_cache, dim=0)
+    #             layer_values = torch.cat(value_cache, dim=0)
+    #             cache.update(layer_keys, layer_values, idx)
+    #     return cache
+
+    # def batch_repeat_interleave(self, repeats: int):
+    #     """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+    #     for layer_idx in range(len(self)):
+    #         self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+    #         self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    # def batch_select_indices(self, indices: torch.Tensor):
+    #     """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+    #     for layer_idx in range(len(self)):
+    #         self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+    #         self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+
+try:
+    from fla.ops.rwkv7.chunk import chunk_rwkv7
+    from fla.ops.rwkv7.fused_recurrent import fused_recurrent_rwkv7
+except ImportError:
+    print("Required module is not installed. Please install it using the following commands:")
+    print("pip install -U git+https://github.com/fla-org/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: RWKV7Qwen2Config, 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)
+
+        # 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)
+    
+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)
+
+# # Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
+# def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim:int=1):
+#     B, L = q.size(0), q.size(-2)
+#     cos = cos[:L].unsqueeze(0).expand(B,L,-1).unsqueeze(unsqueeze_dim)
+#     sin = sin[:L].unsqueeze(0).expand(B,L,-1).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 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
+
+class RWKV7Attention(nn.Module):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        C = self.hidden_size = config.hidden_size
+        H = self.num_heads = config.num_attention_heads
+        N = 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
+
+        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
+        lora_rank_decay = config.lora_rank_decay or calc_lora_rank(0.5, 1.8)
+        lora_rank_iclr = config.lora_rank_iclr or calc_lora_rank(0.5, 1.8)
+        lora_rank_value_residual_mix = config.lora_rank_value_residual_mix or calc_lora_rank(0.5, 1.3)
+        lora_rank_gate = config.lora_rank_gate or calc_lora_rank(0.8, 0.6)
+
+        # self.x_r = nn.Parameter(torch.empty(1,1,C))
+        # self.x_w = nn.Parameter(torch.empty(1,1,C))
+        # self.x_k = nn.Parameter(torch.empty(1,1,C))
+        # self.x_v = nn.Parameter(torch.empty(1,1,C))
+        # self.x_a = nn.Parameter(torch.empty(1,1,C))
+        # self.x_g = nn.Parameter(torch.empty(1,1,C))
+
+        self.w0 = nn.Parameter(torch.empty(1,1,C))
+        self.w1 = nn.Parameter(torch.empty(C, lora_rank_decay))
+        self.w2 = nn.Parameter(torch.empty(lora_rank_decay, C))
+
+        self.a0 = nn.Parameter(torch.empty(1,1,C))
+        self.a1 = nn.Parameter(torch.empty(C, lora_rank_iclr))
+        self.a2 = nn.Parameter(torch.empty(lora_rank_iclr, C))
+
+        #if layer_idx > 0:
+        self.v0 = nn.Parameter(torch.empty(1,1,C))
+        self.v1 = nn.Parameter(torch.empty(C, lora_rank_value_residual_mix))
+        self.v2 = nn.Parameter(torch.empty(lora_rank_value_residual_mix, C))
+
+        if config.gate_rank_type == 1:
+            self.gate = nn.Linear(C, C, bias=False)
+        elif config.gate_rank_type == 2:
+            self.g1 = nn.Parameter(torch.empty(C, lora_rank_gate))
+            self.g2 = nn.Parameter(torch.empty(lora_rank_gate, C))
+
+        self.k_k = nn.Parameter(torch.empty(1,1,C))
+        self.k_a = nn.Parameter(torch.empty(1,1,C))
+        self.r_k = nn.Parameter(torch.empty(H,N))
+
+        if self.config.groupnorm_att:
+            self.ln_x = nn.GroupNorm(H, C, eps=self.head_dim * 1e-5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        v_first: Optional[torch.Tensor] = None, 
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[RWKV7State] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ):
+        if attention_mask is not None:
+            assert len(attention_mask.shape) == 2, (
+                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
+                "for padding purposes (0 indicating padding). "
+                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
+            )
+        
+        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
+        q_len = T
+
+        if use_cache and past_key_values is not None and len(past_key_values) > self.layer_idx:
+            input_vk_state, input_shift_state = past_key_values[self.layer_idx]
+        else:
+            input_vk_state, input_shift_state = torch.zeros(B,H,N,N, dtype=torch.float32,device=x.device), torch.zeros_like(x[:, -1:])
+
+        # NOTE - no need for this without tokenshift
+        # if attention_mask is not None:
+        #     hidden_states = hidden_states.mul(attention_mask[:, -hidden_states.shape[-2]:, None])
+
+        # shifted = torch.cat([input_shift_state, x[:, :-1]], dim=1)
+        # xx = shifted - x
+
+        # xr = x+xx*self.x_r
+        # xw = x+xx*self.x_w
+        # xk = x+xx*self.x_k
+        # xv = x+xx*self.x_v
+        # xa = x+xx*self.x_a
+        # xg = x+xx*self.x_g
+
+        xr = xw = xk = xv = xa = xg = x
+
+        r = self.q_proj(xr)
+        w_lora_result = self.w0 + (torch.tanh(xw @ self.w1) @ self.w2).float()
+        k = self.k_proj(xk)
+        v = self.v_proj(xv)
+        a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2)
+        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)
+            # r = r.transpose(1,2) # BHTN
+            # k = k.transpose(1,2) # B(kvh)TN
+            cos, sin = position_embeddings
+            # cos, sin = shared.angles.unbind(0)
+            r, k = apply_rotary_pos_emb(r, k, cos, sin, unsqueeze_dim=2)
+            # r = r.transpose(1,2).view(B,T,-1).to(v.dtype)
+            # k = k.transpose(1,2).view(B,T,-1).to(v.dtype)
+
+        # 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
+
+        use_alt = False #True
+
+        #kk = torch.nn.functional.normalize((k * self.k_k).view(B,T,H,-1), dim=-1, p=2.0).view(B,T,-1)
+        #kk = torch.nn.functional.normalize((k).view(B,T,H,-1), dim=-1, p=2.0).view(B,T,-1)
+        #kk = k.view(B,T,H,-1)
+        #kk = (kk.float() / (torch.norm(kk.float(), dim=-1, keepdim=True) + 1e-12)).view(B,T,-1).to(k.dtype)
+
+        if use_alt:
+            #kk = torch.nn.functional.normalize((k).view(B,T,H,-1), dim=-1, p=2.0, eps=self.head_dim*1e-5).view(B,T,-1)
+            #kk = torch.nn.functional.normalize((k * self.k_k).view(B,T,H,-1), dim=-1, p=2.0, eps=self.head_dim*1e-5).view(B,T,-1)
+            kk = torch.nn.functional.normalize((k * self.k_k).view(B,T,H,-1), dim=-1, p=2.0).view(B,T,-1)
+            a = (1 + (a-1) * self.k_a)
+            k = k * a
+        else:
+            #kk = torch.nn.functional.normalize((k * self.k_k).view(B,T,H,-1), dim=-1, p=2.0).view(B,T,-1)
+            kk = (k * self.k_k).view(B,T,H,-1).float()
+            kk = (kk / (torch.norm(kk, dim=-1, keepdim=True) + 1e-12)).view(B,T,-1).to(k.dtype)
+            k = k * (1 + (a-1) * self.k_a)
+        # a = 1 + (a-1) * self.k_a
+        # k = k * a
+        if self.layer_idx == 0: v_first = v
+        else: v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2)        
+
+        # dealing with left-padding
+        if attention_mask is not None:
+            v = v * attention_mask[:, -v.shape[-2]:, None]
+            
+        # if T == 1 or not self.training:
+        #     w = torch.exp(-0.606531 * torch.sigmoid(w_lora_result)) # 0.606531 = exp(-0.5)
+        #     output_vk_state = input_vk_state
+        #     for t in range(T):
+        #         r_, w_, k_, v_, kk_, a_ = r[:,t], w[:,t], k[:,t], v[:,t], kk[:,t], a[:,t]
+        #         vk = v_.view(B,H,N,1) @ k_.view(B,H,1,N)
+        #         ab = (-kk_).view(B,H,N,1) @ (kk_*a_).view(B,H,1,N)
+        #         output_vk_state = output_vk_state * w_.view(B,H,1,N) + output_vk_state @ ab.float() + vk.float()
+        #         x[:,t] = (output_vk_state.to(dtype=x.dtype) @ r_.view(B,H,N,1)).view(B,H*N)
+        #     # FIXME - support fast triton kernel for non-training pre-fill with state in and out
+        # else:
+        # FIXME - can simplify to 
+        # log_w = -math.exp(-0.5) * torch.sigmoid(w_lora_result.float())
+        log_neglog_w = - 0.5 - torch.nn.functional.softplus(-w_lora_result)
+        log_w = -log_neglog_w.float().exp()
+
+        if self.config.balance_state:
+            w = log_w.exp()
+            k = k * (1-w+a)
+        elif use_alt:
+            w = log_w.exp()
+            k = kk * (a*w+1-w)
+
+        r,log_w,k,v,kk,a = [i.view(B,T,self.num_heads,-1) for i in [r,log_w,k,v,kk,a]]
+        if self.training:
+            x, output_vk_state = chunk_rwkv7(r, log_w, k, v, -kk, kk*a, initial_state=input_vk_state, output_final_state=use_cache)
+        else:
+            x, output_vk_state = fused_recurrent_rwkv7(r, log_w, k, v, -kk, kk*a, initial_state=input_vk_state, output_final_state=use_cache)
+
+        if self.config.groupnorm_att:
+            x = torch.nn.functional.group_norm(x.view(B*T,H*N).float(), num_groups=H, weight=self.ln_x.weight.float(), bias=self.ln_x.bias.float(), eps = self.ln_x.eps).view(B,T,H*N).to(x.dtype)
+        else:
+            x = x.view(B,T,H*N).to(x.dtype) * N ** -0.5
+        # x = x + ((r.view(B,T,H,-1)*k.view(B,T,H,-1)*self.r_k).sum(dim=-1, keepdim=True) * v.view(B,T,H,-1)).view(B,T,C)
+        x = self.o_proj(x * g)
+
+        output_final_state = not self.training and use_cache and past_key_values is not None
+        if output_final_state:
+            past_key_values.update(output_vk_state, output_shift_state, q_len, self.layer_idx)
+
+        return x, v_first
+    
+class RWKV7Qwen2DecoderLayer(nn.Module):
+    def __init__(self, config: RWKV7Qwen2Config, layer_idx: int):
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+
+        if layer_idx >= config.num_hidden_layers - config.num_attention_layers:
+            self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx)
+        else:
+            self.self_attn = RWKV7Attention(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,
+        v_first: Optional[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,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, v_first = self.self_attn(
+            hidden_states=hidden_states,
+            v_first=v_first,
+            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 = 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, v_first,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+    
+
+RWKV7QWEN2_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 ([`RWKV7Qwen2Config`]):
+            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.",
+    RWKV7QWEN2_START_DOCSTRING,
+)
+class RWKV7Qwen2PreTrainedModel(PreTrainedModel):
+    config_class = RWKV7Qwen2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RWKV7Qwen2DecoderLayer"]
+    _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_()
+
+
+RWKV7QWEN2_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 RWKV7Qwen2 Model outputting raw hidden-states without any specific head on top.",
+    RWKV7QWEN2_START_DOCSTRING,
+)
+class RWKV7Qwen2Model(RWKV7Qwen2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
+
+    Args:
+        config: RWKV7Qwen2Config
+    """
+
+    def __init__(self, config: RWKV7Qwen2Config):
+        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(
+            [RWKV7Qwen2DecoderLayer(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(RWKV7QWEN2_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:
+            if 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, RWKV7State):
+            past_key_values = RWKV7State()
+
+        #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
+        v_first = 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,
+                    v_first,
+                )
+            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,
+                    v_first=v_first,
+                )
+
+            hidden_states = layer_outputs[0]
+            v_first = layer_outputs[1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[2],)
+
+        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 RWKV7Qwen2ForCausalLM(RWKV7Qwen2PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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, RWKV7Qwen2ForCausalLM
+
+        >>> model = RWKV7Qwen2ForCausalLM.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,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[Cache] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        # only last token for `inputs_ids` if the `past_key_values` is not empty.
+        if past_key_values is not None and len(past_key_values) > 0:
+            input_ids = input_ids[:, -1:]
+
+        model_inputs = {
+            'past_key_values': past_key_values,
+            'attention_mask': attention_mask,
+            'cache_position': cache_position,
+        }
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs['inputs_embeds'] = inputs_embeds
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs['input_ids'] = input_ids.contiguous()
+
+        model_inputs.update(**kwargs)
+
+        # 8. Remove unexpected `generate` inputs (TODO @joao: fix trainer and examples)
+        model_inputs.pop("labels", None)
+
+        return model_inputs        
+
+@add_start_docstrings(
+    """
+    The RWKV7Qwen2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`RWKV7Qwen2ForSequenceClassification`] 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).
+    """,
+    RWKV7QWEN2_START_DOCSTRING,
+)
+class RWKV7Qwen2ForSequenceClassification(RWKV7Qwen2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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 RWKV7Qwen2 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.
+    """,
+    RWKV7QWEN2_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->RWKV7Qwen2, LLAMA->RWKV7QWEN2
+class RWKV7Qwen2ForTokenClassification(RWKV7Qwen2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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 RWKV7Qwen2 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`).
+    """,
+    RWKV7QWEN2_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralForQuestionAnswering with Mistral->RWKV7Qwen2, MISTRAL->RWKV7QWEN2
+class RWKV7Qwen2ForQuestionAnswering(RWKV7Qwen2PreTrainedModel):
+    base_model_prefix = "model"
+
+    # Copied from models.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->RWKV7Qwen2
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RWKV7Qwen2Model(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(RWKV7QWEN2_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,
+        )

tokenization_rwkv7qwen2.py

@@ -0,0 +1,4 @@
+from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer
+
+class RWKV6Qwen2Tokenizer(Qwen2Tokenizer):
+    pass

tokenization_rwkv7qwen2_fast.py

@@ -0,0 +1,4 @@
+from transformers.models.qwen2.tokenization_qwen2_fast import Qwen2TokenizerFast
+
+class RWKV6Qwen2TokenizerFast(Qwen2TokenizerFast):
+    pass

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}