modeling_rwkv5.py

# 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."""

import math
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
import torch.nn.functional as F
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"
_CONFIG_FOR_DOC = "Rwkv5Config"

RWKV_PRETRAINED_MODEL_ARCHIVE_LIST = [
    
]

def rwkv_linear_attention_v5_0(H, S, T, hidden, time_decay, time_first, receptance, key, value, lxw, lxb, ow, state, return_state=False, seq_mode=True):
    time_decay = torch.exp(-torch.exp(time_decay.float())).reshape(-1,1,1)
    time_first = torch.exp(time_first.float()).reshape(-1,1,1)
    lxw = lxw.float()
    lxb = lxb.float()

    if seq_mode:
        w = time_decay.reshape(-1, 1)
        u = time_first.reshape(-1, 1)
        ws = w.pow(T).reshape(H, 1, 1)
        ind = torch.arange(T-1, -1, -1, device=w.device).unsqueeze(0).repeat(H, 1)
        w = w.repeat(1, T).pow(ind)
        wk = w.reshape(H, 1, T)
        wb = wk.transpose(-2, -1).flip(1)
        w = torch.cat([w[:, 1:], u], dim=1)
        w = F.pad(w, (0, T))
        w = torch.tile(w, [T])
        w = w[:, :-T].reshape(-1, T, 2 * T - 1)
        w = w[:, :, T-1:].reshape(H, T, T)
        out = ((receptance @ key) * w) @ value + (receptance @ state) * wb
        state = ws * state + (key * wk) @ value
        
        out = out.transpose(1, 2).contiguous().reshape(T, H*S)
        out = F.group_norm(out, num_groups=H, weight=lxw, bias=lxb)
        out = out.to(dtype=hidden.dtype)
        out = out @ ow
    else:
        a = key @ value
        out = receptance @ (time_first * a + state)
        state = a + time_decay * state
        out = out.flatten()
        out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lxw, bias=lxb)
        out = out.to(dtype=hidden.dtype)
        out = out @ ow

    return out, state

cnt = 0

def rwkv_linear_attention_v5_2(B, H, S, T, n_head, hidden, time_decay, time_first, receptance, key, value, gate, lxw, lxb, ow, state, return_state=False, seq_mode=True):
    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()
    # if seq_mode:
    out = torch.empty((B, T, H, S), dtype=receptance.dtype, device=receptance.device)
    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)
        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
    # else:
    #     a = key @ value
    #     # print('key.shape: ', key.shape)
    #     # print('value.shape: ', value.shape)
    #     # print('receptance.shape: ', receptance.shape)
    #     # print('a.shape: ', a.shape)
    #     # print('time_first.shape: ', time_first.shape)
    #     # print('(time_first * a).shape: ', (time_first * a).shape)
    #     # print('time_decay.shape: ', time_decay.shape)
    #     # print('state.shape: ', state.shape)
    #     out = receptance @ (time_first * a + state)
    #     # print('out.shape: ', out.shape)
    #     state = a + time_decay * state
    #     # print('state.shape: ', state.shape)
    #     out = out.reshape(B, H*S)
    #     out = F.group_norm(out, num_groups=H, weight=lxw, bias=lxb).reshape(B, 1, 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
        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

        if self.config.model_version == "5_2":
            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))
        else:
            self.time_decay = nn.Parameter(torch.empty(num_attention_heads))
            self.time_first = nn.Parameter(torch.empty(num_attention_heads))

        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)
        if self.config.model_version == "5_2":
            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)
        if self.config.model_version == "5_2":
            gate = hidden* self.time_mix_gate + shifted * (1 - self.time_mix_gate)

        # if hidden.size(1) == 1 and state is not None:
        #     receptance = self.receptance(receptance).to(torch.float32).view(B, H, 1, S)
        #     key = self.key(key).to(torch.float32).view(B, H, S, 1)
        #     value = self.value(value).to(torch.float32).view(B, H, 1, S)
        # else:
        # https://github.com/BlinkDL/ChatRWKV/blob/main/rwkv_pip_package/src/rwkv/model.py#L693
        key = self.key(key).to(torch.float32).view(B, T, H, S).transpose(1, 2).transpose(-2, -1)
        value = self.value(value).to(torch.float32).view(B, T, H, S).transpose(1, 2)
        receptance = self.receptance(receptance).to(torch.float32).view(B, T, H, S).transpose(1, 2)

        if self.config.model_version == "5_2":
            gate = F.silu(self.gate(gate))
        
        if state is not None:
            state[0][:, :, self.layer_id] = hidden[:, -1]
        
        if self.config.model_version == "5_2":
            return receptance, key, value, gate, state
        return receptance, key, value, 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]

        if self.config.model_version == "5_2":
            receptance, key, value, gate, state = self.extract_key_value(B, H, S, T, hidden, state=state)
        else:
            receptance, key, value, state = self.extract_key_value(H, S, T, hidden, state=state)
        layer_state = state[1][:, :, :, :, self.layer_id] if state is not None else None
        if self.config.model_version == "5_2":
            rwkv, layer_state = rwkv_linear_attention_v5_2(
            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,
            return_state=use_cache,
            seq_mode=seq_mode,
        )
        else:
            rwkv, layer_state = rwkv_linear_attention_v5_0(
                H,
                S,
                T,
                hidden,
                self.time_decay,
                self.time_first,
                receptance,
                key,
                value,
                self.ln_x.weight,
                self.ln_x.bias,
                self.output.weight.t(),
                state=layer_state,
                return_state=use_cache,
                seq_mode=seq_mode,
            )

        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
        if self.config.model_version == "5_2":
            intermediate_size = (
                config.intermediate_size if config.intermediate_size is not None else int((config.hidden_size * 3.5) // 32 * 32)
            )
        else:
            intermediate_size = (
                config.intermediate_size if config.intermediate_size is not None else 4 * config.hidden_size
            )

        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):

        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 RwkvPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = Rwkv5Config
    base_model_prefix = "transformer"
    _no_split_modules = ["RwkvBlock"]
    _keep_in_fp32_modules = ["time_decay", "time_first"]

    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.head_size

            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, :]

            if module.config.model_version == "5_2":
                # 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)
                ]
            else:
                # https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4neo/src/model.py#L172
                decay_speed = [
                    -6.0 + 5.0 * (h / (num_attention_heads - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
                    for h in range(num_attention_heads)
                ]
            decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
            if module.config.model_version == "5_2":
                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,
                    )
                )
            else:
                tmp = torch.ones(num_attention_heads) * (-3.0)

            with torch.no_grad():
                if module.config.model_version == "5_2":
                    module.time_decay.data = decay_speed.reshape(num_attention_heads, module.config.head_size)
                    module.time_faaaa.data = tmp.reshape(num_attention_heads, module.config.head_size)
                else:
                    module.time_decay.data = decay_speed
                    module.time_first.data = tmp

                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)
                if module.config.model_version == "5_2":
                    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)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, RwkvModel):
            module.gradient_checkpointing = value


@dataclass
class RwkvOutput(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 RwkvCausalLMOutput(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
    last_hidden_state: 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 RwkvModel(RwkvPreTrainedModel):
    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.pre_ln_flag = 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=RwkvOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        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, RwkvOutput]:
        seq_mode = input_ids.shape[1] > 1
        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 if not self.training else False)
        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:
            if not self.pre_ln_flag:
                normalized_weight = F.layer_norm(self.embeddings.weight, (self.config.hidden_size, ), weight=self.blocks[0].pre_ln.weight, bias=self.blocks[0].pre_ln.bias)
                self.embeddings.weight = nn.Parameter(normalized_weight)
                self.pre_ln_flag = True
            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.head_size
            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())


        hidden_states = inputs_embeds
        global cnt
        cnt += 1
        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 RwkvOutput(
            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:
                        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 RwkvForCausalLM(RwkvPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.rwkv = RwkvModel(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=RwkvCausalLMOutput,
        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, RwkvCausalLMOutput]:
        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,
        )
        last_hidden_state = rwkv_outputs.last_hidden_state
        state = rwkv_outputs.state

        logits = self.head(last_hidden_state)

        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 RwkvCausalLMOutput(
            loss=loss,
            logits=logits,
            state=rwkv_outputs.state,
            last_hidden_state=rwkv_outputs.last_hidden_state,
            attentions=rwkv_outputs.attentions,
        )