Delete modeling_rwkv5.py

modeling_rwkv5.py

@@ -1,862 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Bo Peng and HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  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.
-"""PyTorch RWKV5 World model."""
-
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_ninja_available,
-    is_torch_cuda_available,
-    logging,
-)
-
-from .configuration_rwkv5 import Rwkv5Config
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "RWKV/rwkv-5-world-1b5"
-_CONFIG_FOR_DOC = "Rwkv5Config"
-
-RWKV5_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "RWKV/rwkv-5-world-1b5",
-    "RWKV/rwkv-5-world-3b",
-    # See all RWKV models at https://huggingface.co/models?filter=rwkv
-]
-
-rwkv5_cuda_kernel = None
-
-
-def load_wkv5_cuda_kernel(head_size):
-    from torch.utils.cpp_extension import load as load_kernel
-
-    global rwkv5_cuda_kernel
-
-    kernel_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "rwkv5"
-    cuda_kernel_files = [kernel_folder / f for f in ["wkv5_op.cpp", "wkv5_cuda.cu"]]
-
-    # Only load the kernel if it's not been loaded yet or if we changed the context length
-    if rwkv5_cuda_kernel is not None and rwkv5_cuda_kernel.head_size == head_size:
-        return
-
-    logger.info(f"Loading CUDA kernel for RWKV at head size of {head_size}.")
-
-    flags = [
-        "-res-usage",
-        "--maxrregcount 60",
-        "--use_fast_math",
-        "-O3",
-        "-Xptxas -O3",
-        "--extra-device-vectorization",
-        f"-D_N_={head_size}",
-    ]
-    rwkv5_cuda_kernel = load_kernel(
-        name=f"wkv_{head_size}",
-        sources=cuda_kernel_files,
-        verbose=(logging.get_verbosity() == logging.DEBUG),
-        extra_cuda_cflags=flags,
-    )
-    rwkv5_cuda_kernel.head_size = head_size
-
-
-class WKV_5(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, B, T, C, H, r, k, v, w, u, s):
-        with torch.no_grad():
-            assert r.dtype == torch.bfloat16
-            assert k.dtype == torch.bfloat16
-            assert v.dtype == torch.bfloat16
-            assert w.dtype == torch.bfloat16
-            assert u.dtype == torch.bfloat16
-            assert s.dtype == torch.float32
-            ctx.B = B
-            ctx.T = T
-            ctx.C = C
-            ctx.H = H
-            assert r.is_contiguous()
-            assert k.is_contiguous()
-            assert v.is_contiguous()
-            assert w.is_contiguous()
-            assert u.is_contiguous()
-            ew = (-torch.exp(w.float())).contiguous()
-            eew = (torch.exp(ew)).contiguous()
-            ctx.save_for_backward(r, k, v, eew, ew, u)
-            y = torch.empty(
-                (B, T, C), device=r.device, dtype=torch.bfloat16, memory_format=torch.contiguous_format
-            )  # .uniform_(-1, 1)
-            rwkv5_cuda_kernel.forward(B, T, C, H, r, k, v, eew, u, y, s)
-            return y, s
-
-    @staticmethod
-    def backward(ctx, gy):
-        with torch.no_grad():
-            assert gy.dtype == torch.bfloat16
-            B = ctx.B
-            T = ctx.T
-            C = ctx.C
-            H = ctx.H
-            assert gy.is_contiguous()
-            r, k, v, eew, ew, u = ctx.saved_tensors
-            gr = torch.empty(
-                (B, T, C),
-                device=gy.device,
-                requires_grad=False,
-                dtype=torch.bfloat16,
-                memory_format=torch.contiguous_format,
-            )  # .uniform_(-1, 1)
-            gk = torch.empty(
-                (B, T, C),
-                device=gy.device,
-                requires_grad=False,
-                dtype=torch.bfloat16,
-                memory_format=torch.contiguous_format,
-            )  # .uniform_(-1, 1)
-            gv = torch.empty(
-                (B, T, C),
-                device=gy.device,
-                requires_grad=False,
-                dtype=torch.bfloat16,
-                memory_format=torch.contiguous_format,
-            )  # .uniform_(-1, 1)
-            gw = torch.empty(
-                (B, C),
-                device=gy.device,
-                requires_grad=False,
-                dtype=torch.bfloat16,
-                memory_format=torch.contiguous_format,
-            )  # .uniform_(-1, 1)
-            gu = torch.empty(
-                (B, C),
-                device=gy.device,
-                requires_grad=False,
-                dtype=torch.bfloat16,
-                memory_format=torch.contiguous_format,
-            )  # .uniform_(-1, 1)
-            rwkv5_cuda_kernel.backward(B, T, C, H, r, k, v, eew, ew, u, gy, gr, gk, gv, gw, gu)
-            gw = torch.sum(gw, 0).view(H, C // H)
-            gu = torch.sum(gu, 0).view(H, C // H)
-            return (None, None, None, None, gr, gk, gv, gw, gu)
-
-
-def rwkv_linear_attention_v5_cpu(
-    B,
-    H,
-    S,
-    T,
-    n_head,
-    hidden,
-    time_decay,
-    time_first,
-    receptance,
-    key,
-    value,
-    gate,
-    lxw,
-    lxb,
-    ow,
-    state,
-):
-    key = key.to(torch.float32).view(B, T, H, S).transpose(1, 2).transpose(-2, -1)
-    value = value.to(torch.float32).view(B, T, H, S).transpose(1, 2)
-    receptance = receptance.to(torch.float32).view(B, T, H, S).transpose(1, 2)
-    time_decay = torch.exp(-torch.exp(time_decay.float())).reshape(-1, 1, 1).reshape(n_head, -1, 1)
-    time_first = time_first.float().reshape(-1, 1, 1).reshape(n_head, -1, 1)
-    lxw = lxw.float()
-    lxb = lxb.float()
-    out = torch.zeros_like(key).reshape(B, T, H, S)
-    for t in range(T):
-        rt = receptance[:, :, t : t + 1, :]
-        kt = key[:, :, :, t : t + 1]
-        vt = value[:, :, t : t + 1, :]
-        at = kt @ vt
-        out[:, t] = (rt @ (time_first * at + state)).squeeze(2)
-        with torch.no_grad():
-            state = at + time_decay * state
-
-    out = out.reshape(B * T, H * S)
-    out = F.group_norm(out, num_groups=H, weight=lxw, bias=lxb).reshape(B, T, H * S)
-    out = out.to(dtype=hidden.dtype) * gate
-    out = out @ ow
-
-    return out, state
-
-
-def rwkv_linear_attention(
-    B,
-    H,
-    S,
-    T,
-    n_head,
-    hidden,
-    time_decay,
-    time_first,
-    receptance,
-    key,
-    value,
-    gate,
-    lxw,
-    lxb,
-    ow,
-    state,
-):
-    no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, receptance, key, value])
-    # Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version
-    # in this case).
-    one_token = key.size(1) == 1
-    if rwkv5_cuda_kernel is None or no_cuda or one_token:
-        return rwkv_linear_attention_v5_cpu(
-            B,
-            H,
-            S,
-            T,
-            n_head,
-            hidden,
-            time_decay,
-            time_first,
-            receptance,
-            key,
-            value,
-            gate,
-            lxw,
-            lxb,
-            ow,
-            state,
-        )
-    else:
-        out, state = WKV_5.apply(B, T, H * S, H, receptance, key, value, time_decay, time_first, state)
-        out = out.reshape(B * T, H * S)
-        out = F.group_norm(out, num_groups=H, weight=lxw, bias=lxb).reshape(B, T, H * S)
-        out = out.to(dtype=hidden.dtype) * gate
-        out = out @ ow
-        return out, state
-
-
-class RwkvSelfAttention(nn.Module):
-    def __init__(self, config, layer_id=0):
-        super().__init__()
-        self.config = config
-        kernel_loaded = rwkv5_cuda_kernel is not None and rwkv5_cuda_kernel.head_size == config.head_size
-        if is_ninja_available() and is_torch_cuda_available() and not kernel_loaded:
-            try:
-                load_wkv5_cuda_kernel(config.context_length)
-            except Exception:
-                logger.info("Could not load the custom CUDA kernel for RWKV5 attention.")
-        self.layer_id = layer_id
-        hidden_size = config.hidden_size
-        # https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4neo/src/model.py#L146
-        num_attention_heads = hidden_size // config.head_size
-        self.num_attention_heads = num_attention_heads
-        attention_hidden_size = (
-            config.attention_hidden_size if config.attention_hidden_size is not None else hidden_size
-        )
-        self.attention_hidden_size = attention_hidden_size
-
-        self.time_decay = nn.Parameter(torch.empty(num_attention_heads, config.head_size))
-        self.time_faaaa = nn.Parameter(torch.empty(num_attention_heads, config.head_size))
-        self.time_mix_gate = nn.Parameter(torch.empty(1, 1, hidden_size))
-
-        self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
-        self.time_mix_value = nn.Parameter(torch.empty(1, 1, hidden_size))
-        self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
-
-        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
-        self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False)
-        self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False)
-        self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False)
-        self.gate = nn.Linear(hidden_size, attention_hidden_size, bias=False)
-        self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False)
-        # https://github.com/BlinkDL/RWKV-LM/blob/3db37a72356b736966ddd377268f02b80963af3f/RWKV-v4neo/src/model.py#L190C1-L190C1
-        self.ln_x = nn.GroupNorm(hidden_size // config.head_size, hidden_size)
-
-    # TODO: maybe jit, otherwise move inside forward
-    def extract_key_value(self, B, H, S, T, hidden, state=None):
-        # Mix hidden with the previous timestep to produce key, value, receptance
-        if hidden.size(1) == 1 and state is not None:
-            shifted = state[0][:, :, self.layer_id]
-        else:
-            shifted = self.time_shift(hidden)
-            if state is not None:
-                shifted[:, 0] = state[0][:, :, self.layer_id]
-        if len(shifted.size()) == 2:
-            shifted = shifted.unsqueeze(1)
-        key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
-        value = hidden * self.time_mix_value + shifted * (1 - self.time_mix_value)
-        receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
-        gate = hidden * self.time_mix_gate + shifted * (1 - self.time_mix_gate)
-
-        # https://github.com/BlinkDL/ChatRWKV/blob/main/rwkv_pip_package/src/rwkv/model.py#L693
-        key = self.key(key)
-        value = self.value(value)
-        receptance = self.receptance(receptance)
-        gate = F.silu(self.gate(gate))
-
-        if state is not None:
-            state[0][:, :, self.layer_id] = hidden[:, -1]
-
-        return receptance, key, value, gate, state
-
-    def forward(self, hidden, state=None, use_cache=False, seq_mode=True):
-        B = hidden.shape[0]
-        H = self.time_decay.shape[0]
-        S = hidden.shape[-1] // H
-        T = hidden.shape[1]
-
-        receptance, key, value, gate, state = self.extract_key_value(B, H, S, T, hidden, state=state)
-        layer_state = state[1][:, :, :, :, self.layer_id] if state is not None else None
-        rwkv, layer_state = rwkv_linear_attention(
-            B,
-            H,
-            S,
-            T,
-            self.num_attention_heads,
-            hidden,
-            self.time_decay,
-            self.time_faaaa,
-            receptance,
-            key,
-            value,
-            gate,
-            self.ln_x.weight,
-            self.ln_x.bias,
-            self.output.weight.t(),
-            state=layer_state,
-        )
-
-        if layer_state is not None:
-            state[1][:, :, :, :, self.layer_id] = layer_state
-
-        return rwkv, state
-
-
-class RwkvFeedForward(nn.Module):
-    def __init__(self, config, layer_id=0):
-        super().__init__()
-        self.config = config
-        self.layer_id = layer_id
-        hidden_size = config.hidden_size
-        # https://github.com/BlinkDL/RWKV-LM/blob/3db37a72356b736966ddd377268f02b80963af3f/RWKV-v4neo/train.py#L168
-        intermediate_size = (
-            config.intermediate_size
-            if config.intermediate_size is not None
-            else int((config.hidden_size * 3.5) // 32 * 32)
-        )
-
-        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
-        self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
-        self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
-
-        self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
-        self.value = nn.Linear(intermediate_size, hidden_size, bias=False)
-
-    def forward(self, hidden, state=None):
-        if hidden.size(1) == 1 and state is not None:
-            shifted = state[2][:, :, self.layer_id]
-        else:
-            shifted = self.time_shift(hidden)
-            if state is not None:
-                shifted[:, 0] = state[2][:, :, self.layer_id]
-        if len(shifted.size()) == 2:
-            shifted = shifted.unsqueeze(1)
-        key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
-        receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
-
-        key = torch.square(torch.relu(self.key(key)))
-        value = self.value(key)
-        receptance = torch.sigmoid(self.receptance(receptance))
-
-        if state is not None:
-            state[2][:, :, self.layer_id] = hidden[:, -1]
-
-        return receptance * value, state
-
-
-class RwkvBlock(nn.Module):
-    def __init__(self, config, layer_id):
-        super().__init__()
-        self.config = config
-        self.layer_id = layer_id
-
-        if layer_id == 0:
-            self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
-
-        self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
-        self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
-
-        self.attention = RwkvSelfAttention(config, layer_id)
-        self.feed_forward = RwkvFeedForward(config, layer_id)
-
-    def forward(self, hidden, state=None, use_cache=False, output_attentions=False, seq_mode=True):
-        if self.layer_id == 0:
-            hidden = self.pre_ln(hidden)
-        attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache, seq_mode=seq_mode)
-        hidden = hidden + attention
-
-        feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
-        hidden = hidden + feed_forward
-
-        outputs = (hidden, state)
-        if output_attentions:
-            outputs += (attention,)
-        else:
-            outputs += (None,)
-
-        return outputs
-
-
-class Rwkv5PreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = Rwkv5Config
-    base_model_prefix = "rwkv"
-    _no_split_modules = ["RwkvBlock"]
-    _keep_in_fp32_modules = ["time_decay", "time_first"]
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, RwkvSelfAttention):
-            layer_id = module.layer_id
-            num_hidden_layers = module.config.num_hidden_layers
-            hidden_size = module.config.hidden_size
-            attention_hidden_size = module.attention_hidden_size
-            num_attention_heads = hidden_size // module.config.num_attention_heads
-
-            ratio_0_to_1 = layer_id / (num_hidden_layers - 1)  # 0 to 1
-            ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers)  # 1 to ~0
-
-            time_weight = torch.tensor(
-                [i / hidden_size for i in range(hidden_size)],
-                dtype=module.time_mix_key.dtype,
-                device=module.time_mix_key.device,
-            )
-            time_weight = time_weight[None, None, :]
-
-            # https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4neo/src/model.py#L398
-            decay_speed = [
-                -6.0 + 5.0 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
-                for h in range(attention_hidden_size)
-            ]
-            decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
-            tmp = torch.tensor(
-                [
-                    (1.0 - (i / (attention_hidden_size - 1.0))) * ratio_0_to_1 + 0.1 * ((i + 1) % 3 - 1)
-                    for i in range(attention_hidden_size)
-                ],
-                dtype=module.time_faaaa.dtype,
-                device=module.time_faaaa.device,
-            )
-
-            with torch.no_grad():
-                module.time_decay.data = decay_speed.reshape(num_attention_heads, module.config.num_attention_heads)
-                module.time_faaaa.data = tmp.reshape(num_attention_heads, module.config.num_attention_heads)
-                module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
-
-                module.time_mix_value.data = torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
-                module.time_mix_receptance.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
-                module.time_mix_gate.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
-
-        elif isinstance(module, RwkvFeedForward):
-            layer_id = module.layer_id
-            num_hidden_layers = module.config.num_hidden_layers
-            hidden_size = module.config.hidden_size
-
-            ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers)  # 1 to ~0
-
-            time_weight = torch.tensor(
-                [i / hidden_size for i in range(hidden_size)],
-                dtype=module.time_mix_key.dtype,
-                device=module.time_mix_key.device,
-            )
-            time_weight = time_weight[None, None, :]
-
-            with torch.no_grad():
-                module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
-                module.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0)
-
-
-@dataclass
-class Rwkv5Output(ModelOutput):
-    """
-    Class for the RWKV model outputs.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
-            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
-            avoid providing the old `input_ids`.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
-            the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    state: Optional[List[torch.FloatTensor]] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class Rwkv5CausalLMOutput(ModelOutput):
-    """
-    Base class for causal language model (or autoregressive) outputs.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Language modeling loss (for next-token prediction).
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
-            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
-            avoid providing the old `input_ids`.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
-            the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    state: Optional[List[torch.FloatTensor]] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-RWKV_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 ([`Rwkv5Config`]): 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.
-"""
-
-RWKV_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
-            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
-            `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
-            sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their
-            past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See
-            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
-            IDs?](../glossary#input-ids)
-        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.
-        state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*):
-            If passed along, the model uses the previous state in all the blocks (which will give the output for the
-            `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
-        use_cache (`bool`, *optional*):
-            If set to `True`, the last state is returned and can be used to quickly generate the next logits.
-        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.
-"""
-
-
-@add_start_docstrings(
-    "The bare RWKV Model transformer outputting raw hidden-states without any specific head on top.",
-    RWKV_START_DOCSTRING,
-)
-class Rwkv5Model(Rwkv5PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.blocks = nn.ModuleList([RwkvBlock(config, layer_id=idx) for idx in range(config.num_hidden_layers)])
-        self.ln_out = nn.LayerNorm(config.hidden_size)
-
-        self.layers_are_rescaled = False
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings
-
-    def set_input_embeddings(self, new_embeddings):
-        self.embeddings = new_embeddings
-
-    @add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=Rwkv5Output,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.LongTensor] = None,  # noqa
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        state: Optional[List[torch.FloatTensor]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, Rwkv5Output]:
-        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
-        )
-        # rwkv5 only support inference in huggingface.
-        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 self.training == self.layers_are_rescaled and (
-            self.embeddings.weight.dtype == torch.float16 or self.embeddings.weight.dtype == torch.bfloat16
-        ):
-            self._rescale_layers()
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is None and inputs_embeds is None:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embeddings(input_ids)
-
-        if use_cache and state is None:
-            # https://github.com/BlinkDL/ChatRWKV/blob/main/rwkv_pip_package/src/rwkv/model.py#L904-L906
-            state = []
-            num_attention_heads = self.config.hidden_size // self.config.num_attention_heads
-            state.append(
-                torch.zeros(
-                    (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers),
-                    dtype=inputs_embeds.dtype,
-                    requires_grad=False,
-                    device=inputs_embeds.device,
-                ).contiguous()
-            )
-            state.append(
-                torch.zeros(
-                    (
-                        inputs_embeds.size(0),
-                        num_attention_heads,
-                        self.config.hidden_size // num_attention_heads,
-                        self.config.hidden_size // num_attention_heads,
-                        self.config.num_hidden_layers,
-                    ),
-                    dtype=torch.float32,
-                    requires_grad=False,
-                    device=inputs_embeds.device,
-                ).contiguous()
-            )
-            state.append(
-                torch.zeros(
-                    (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers),
-                    dtype=inputs_embeds.dtype,
-                    requires_grad=False,
-                    device=inputs_embeds.device,
-                ).contiguous()
-            )
-
-        seq_mode = inputs_embeds.shape[1] > 1
-        hidden_states = inputs_embeds
-
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        for idx, block in enumerate(self.blocks):
-            hidden_states, state, attentions = block(
-                hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions, seq_mode=seq_mode
-            )
-            if (
-                self.layers_are_rescaled
-                and self.config.rescale_every > 0
-                and (idx + 1) % self.config.rescale_every == 0
-            ):
-                hidden_states = hidden_states / 2
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (attentions,)
-
-        hidden_states = self.ln_out(hidden_states)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return (hidden_states, state, all_hidden_states, all_self_attentions)
-
-        return Rwkv5Output(
-            last_hidden_state=hidden_states,
-            state=state,
-            hidden_states=all_hidden_states,  # None
-            attentions=all_self_attentions,  # None
-        )
-
-    def _rescale_layers(self):
-        # Layers should be rescaled for inference only.
-        if self.layers_are_rescaled == (not self.training):
-            return
-        if self.config.rescale_every > 0:
-            with torch.no_grad():
-                for block_id, block in enumerate(self.blocks):
-                    if self.training:
-                        block.attention.output.weight.mul_(2 ** int(block_id // self.config.rescale_every))
-                        block.feed_forward.value.weight.mul_(2 ** int(block_id // self.config.rescale_every))
-                    else:
-                        # Deal with quantization statistics
-                        if hasattr(block.attention.output.weight, "SCB"):
-                            block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
-                            block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
-                        elif hasattr(block.attention.output.weight, "quant_state"):
-                            self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id)
-                            self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id)
-                        else:
-                            block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
-                            block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))
-
-        self.layers_are_rescaled = not self.training
-
-
-@add_start_docstrings(
-    """
-    The RWKV Model transformer with a language modeling head on top (linear layer with weights tied to the input
-    embeddings).
-    """,
-    RWKV_START_DOCSTRING,
-)
-class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
-    _tied_weights_keys = ["head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.rwkv = Rwkv5Model(config)
-        self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.head = new_embeddings
-
-    def prepare_inputs_for_generation(self, input_ids, state=None, inputs_embeds=None, **kwargs):
-        # only last token for inputs_ids if the state is passed along.
-        if state is not None:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and state is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs["state"] = state
-        return model_inputs
-
-    @add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=Rwkv5CausalLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        state: Optional[List[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, Rwkv5CausalLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
-            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
-            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        rwkv_outputs = self.rwkv(
-            input_ids,
-            inputs_embeds=inputs_embeds,
-            state=state,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = rwkv_outputs[0]
-
-        logits = self.head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + rwkv_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return Rwkv5CausalLMOutput(
-            loss=loss,
-            logits=logits,
-            state=rwkv_outputs.state,
-            hidden_states=rwkv_outputs.hidden_states,
-            attentions=rwkv_outputs.attentions,
-        )