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configuration_rwkv5.py

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+# coding=utf-8
+# Copyright 2023 The OpenAI Team Authors 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.
+""" RWKV configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+RWKV5_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class Rwkv5Config(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`Rwkv5Model`]. It is used to instantiate a RWKV5
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the RWVK-4
+    [RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) architecture.
+
+    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 65536):
+            Vocabulary size of the RWKV5 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Rwkv5Model`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the embeddings and hidden states.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the model.
+        attention_hidden_size (`int`, *optional*):
+            Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            The attention heads to use in rwkv5 self_attention module.
+        head_size (`int`, *optional*, defaults to 64): head_size of rwkv5 self_attention module.
+        intermediate_size (`int`, *optional*):
+            Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            The epsilon to use in the layer normalization layers.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            The id of the beginning of sentence token in the vocabulary. Defaults to 0 as RWKV5 uses the same tokenizer
+            as GPTNeoX.
+        eos_token_id (`int`, *optional*, defaults to 0):
+            The id of the end of sentence token in the vocabulary. Defaults to 0 as RWKV5 uses the same tokenizer as
+            GPTNeoX.
+        rescale_every (`int`, *optional*, defaults to 6):
+            At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
+            `rescale_every` layer. If set to 0 or a negative number, no rescale is done.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether or not to tie the word embeddings with the input token embeddings.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last state.
+
+
+    Example:
+
+    ```python
+    >>> from transformers import Rwkv5Config, Rwkv5Model
+
+    >>> # Initializing a Rwkv5 configuration
+    >>> configuration = Rwkv5Config()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = Rwkv5Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "rwkv5"
+
+    def __init__(
+        self,
+        vocab_size=65536,
+        hidden_size=768,
+        num_hidden_layers=24,
+        attention_hidden_size=None,
+        num_attention_heads=64,
+        head_size=64,
+        intermediate_size=None,
+        layer_norm_epsilon=1e-5,
+        bos_token_id=0,
+        eos_token_id=0,
+        rescale_every=6,
+        tie_word_embeddings=False,
+        use_cache=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.head_size = head_size
+        self.intermediate_size = None
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.rescale_every = rescale_every
+        self.use_cache = use_cache
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
+        )

modeling_rwkv5.py

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+# 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,
+        )