modeling_baichuan.py

# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from typing import List, Optional, Tuple, Union

import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
import torch.utils.checkpoint
from transformers import PreTrainedModel, add_start_docstrings
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
from transformers.utils import logging, add_start_docstrings_to_model_forward, replace_return_docstrings
from transformers.configuration_utils import PretrainedConfig
from einops import rearrange, repeat
from .configuration_baichuan import BaiChuanConfig

logger = logging.get_logger(__name__)
HAS_FLASH_ATTN = False
try:
    from flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache, flash_attn_func
    from flash_attn.layers.rotary import apply_rotary_emb_func

    HAS_FLASH_ATTN = True
except ImportError:
    logger.warning(
        "flash-attention is not installed correctly. "
    )


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
        input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


class RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        RMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=1e6, device=None, interleaved=False):
        super().__init__()
        self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.base = base
        self.dim = dim
        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = 0
        self.interleaved = interleaved

    def forward(self, q, k, seqlen_offset=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.

        seq_len = q.shape[1] + seqlen_offset
        if seq_len > self.max_seq_len_cached:
            self.max_seq_len_cached = seq_len
            self.inv_freq = 1.0 / (
                    self.base ** (torch.arange(0, self.dim, 2).float().to(self.inv_freq.device) / self.dim))
            t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            # freqs = torch.einsum("i,j->ij", t, self.inv_freq) # dont use this, bug in fp16
            freqs = torch.outer(t, self.inv_freq)
            self.cos_cached = freqs.cos().to(q.device)
            self.sin_cached = freqs.sin().to(k.device)

        return apply_rotary_emb_func(
            q.float(), self.cos_cached[seqlen_offset:], self.sin_cached[seqlen_offset:],
            self.interleaved, True  # inplace=True
        ).to(q.dtype), apply_rotary_emb_func(
            k.float(), self.cos_cached[seqlen_offset:], self.sin_cached[seqlen_offset:],
            self.interleaved, True  # inplace=True
        ).to(k.dtype)


class MLP(nn.Module):
    def __init__(
            self,
            hidden_size: int,
            intermediate_size: int,
            hidden_act: str,
    ):
        super().__init__()
        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

def custom_convolution(U, K):
    """
    U: 输入矩阵, 形状为 (bs, seq, h, d)
    K: 卷积核, 形状为 (w, h)
    返回: 输出矩阵 V, 形状为 (bs, seq, h, d)
    """
    h, w = K.shape
    padding = (w - 1, 0) 
    U_padded = F.pad(U, (0, 0, 0, 0, *padding))  # 形状变为 (bs, seq+w-1, h, d)
    U_unfolded = U_padded.unfold(1, w, 1)  # 形状变为 (bs, seq+w-1, h, d, w)
    K  = K.unsqueeze(0).unsqueeze(0).unsqueeze(-2)
    V_unfolded = U_unfolded * K # 形状保持为 (bs, seq, h, d, w)
    V = V_unfolded.sum(dim=-1)  # 形状变为 (bs, seq, h, d)
    return V
class Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: BaiChuanConfig, num_heads):
        super().__init__()
        self.config = config        
        # print(num_heads)
        self.num_heads = config.num_attention_heads
        self.head_dim = config.hidden_size // config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings        
        self.num_kv_heads = config.num_kv_heads
        self.W_pack = nn.Linear(config.hidden_size, self.hidden_size + 2 * self.num_kv_heads * self.head_dim,
                                bias=False)        
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
        self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
        self.cos, self.sin = None, None
        self.window = 2
        self.K = nn.Parameter(torch.softmax(torch.randn((self.num_kv_heads,self.window)),dim=-1))
        self.V = nn.Parameter(torch.softmax(torch.randn((self.num_kv_heads,self.window)),dim=-1))
    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_value: Optional[Tuple[torch.Tensor]] = None,
            output_attentions: bool = False,
            use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

        bsz, seqlen_q, _ = hidden_states.size()
        past_len = 0
        attn_weights = None
        if past_key_value is not None:
            cache_k, cache_v, past_len = past_key_value
            new_len = past_len + hidden_states.size(1)
            if new_len > cache_k.size(1):
                cache_k = torch.cat([cache_k,
                                     torch.empty(bsz, 256, cache_k.size(2), cache_k.size(3), dtype=cache_k.dtype,
                                                 device=cache_k.device)], 1)
                cache_v = torch.cat([cache_v,
                                     torch.empty(bsz, 256, cache_v.size(2), cache_v.size(3), dtype=cache_v.dtype,
                                                 device=cache_v.device)], 1)

        proj = self.W_pack(hidden_states)

        proj = rearrange(proj, 'bs seq_len (n_head head_dim) -> n_head bs seq_len head_dim', head_dim=self.head_dim)
        q = rearrange(proj[:self.num_heads], 'n_head bs seq_len head_dim -> bs seq_len n_head head_dim')
        k = rearrange(proj[self.num_heads:self.num_heads + self.num_kv_heads],
                      'n_head bs seq_len head_dim -> bs seq_len n_head head_dim')
        v = rearrange(proj[self.num_heads + self.num_kv_heads:],
                      'n_head bs seq_len head_dim -> bs seq_len n_head head_dim')
        
        # proj = rearrange(proj, 'bs seq_len (qkv n_head head_dim) -> qkv bs seq_len n_head head_dim',
        #                  head_dim=self.head_dim, n_head=self.num_heads)
        # q, k, v = proj
        if past_key_value is None:
            k = custom_convolution(k, self.K)
            v = custom_convolution(v, self.V)
            self.last_k = k[:,-1:]
            self.last_v = v[:,-1:]
        else:
            self.last_k,k = k, self.K[:,:1]*self.last_k + self.K[:,1:]*k
            self.last_v,v = v, self.V[:,:1]*self.last_v + self.V[:,1:]*v
        q, k = self.rotary_emb(q, k, seqlen_offset=past_len)
        
        if HAS_FLASH_ATTN and q.dtype != torch.float32:
            if past_key_value is not None:
                attn_output = flash_attn_with_kvcache(
                    q,
                    cache_k,
                    cache_v,
                    k,
                    v,
                    causal=False,
                    cache_seqlens=past_len
                )
            else:
                attn_outputs = flash_attn_func(
                    q,
                    k,
                    v,
                    causal=True,
                    return_attn_probs=output_attentions
                )
                attn_output = attn_outputs[0] if output_attentions else attn_outputs
                attn_weights = attn_outputs[2] if output_attentions else None
        else:  # fallback to pytorch impl
            if past_key_value is not None:
                cache_k[:, past_len:new_len] = k
                cache_v[:, past_len:new_len] = v
                attn_output = F.scaled_dot_product_attention(
                    q.transpose(1, 2),
                    cache_k[:, :new_len].transpose(1, 2),
                    cache_v[:, :new_len].transpose(1, 2),
                    attn_mask=attention_mask
                )
            else:
                attn_output = F.scaled_dot_product_attention(
                    q.transpose(1, 2),
                    k.transpose(1, 2),
                    v.transpose(1, 2),
                    attn_mask=attention_mask
                )
            attn_output = attn_output.transpose(1, 2)
        if use_cache:
            if past_key_value is not None:
                past_key_value = (cache_k, cache_v, past_len + seqlen_q)
            else:
                past_key_value = (k, v, seqlen_q)

        attn_output = attn_output.reshape(bsz, seqlen_q, self.hidden_size)
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights, past_key_value


class DecoderLayer(nn.Module):
    def __init__(self, config: BaiChuanConfig, num_heads):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Attention(config=config, num_heads=num_heads)
        self.mlp = MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
        )
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_value: Optional[Tuple[torch.Tensor]] = None,
            output_attentions: Optional[bool] = False,
            use_cache: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class PreTrainedModel(PreTrainedModel):
    config_class = BaiChuanConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DecoderLayer"]
    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

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


class Model(PreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DecoderLayer`]

    Args:
        config: BaiChuanConfig
    """

    def __init__(self, config: BaiChuanConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([DecoderLayer(config, num_heads=self.get_num_heads(layer_id)) for layer_id in
                                     range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()
    def get_num_heads(self, layer_index: int):
        return self.config.num_attention_heads 
        if 0 <= layer_index < self.config.num_hidden_layers // 4 or layer_index == self.config.num_hidden_layers-1:
            return 8
        elif self.config.num_hidden_layers // 4 <= layer_index < self.config.num_hidden_layers // 2:
            return 4
        elif self.config.num_hidden_layers // 2 <= layer_index < self.config.num_hidden_layers*3 // 4:
            return 2
        else:
            return 1
    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                device=inputs_embeds.device,
                past_key_values_length=past_key_values_length,
            )

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
                inputs_embeds.device
            )
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    def forward(
            self,
            input_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(
                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
            )
        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
        )

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class NormHead(nn.Module):
    def __init__(self, hidden_size, vocab_size, bias=False):
        super().__init__()
        self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))

    def forward(self, hidden_states):
        norm_weight = nn.functional.normalize(self.weight)
        return nn.functional.linear(hidden_states, norm_weight)


class BaiChuanForCausalLM(PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.model = Model(config)

        # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def forward(
            self,
            input_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            labels: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, ModelForCausalLM

        >>> model = ModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> prompt = "Hey, are you consciours? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
        ```"""
        # use_cache = False

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
            self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -1:]
        position_ids = kwargs.get("position_ids", None)

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_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 past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
        return reordered_past