# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
# Modified by hiyouga, to support attention mask, the alibi implementation is largely borrowed from
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py

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

import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.utils import logging

from .configuration_baichuan import BaichuanConfig


logger = logging.get_logger(__name__)


# Copied from transformers.models.bloom.modeling_bloom._make_causal_mask
def _make_causal_mask(
    input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
) -> torch.BoolTensor:
    """
    Make causal mask used for self-attention.
    """
    batch_size, target_length = input_ids_shape
    mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
    # ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
    seq_ids = torch.arange(target_length, device=device)
    mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]

    if past_key_values_length > 0:
        mask[:, :past_key_values_length] = False

    expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
    return expanded_mask


# Copied from transformers.models.bloom.modeling_bloom._expand_mask
def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
    """
    Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
    """
    batch_size, src_length = mask.shape
    tgt_length = tgt_length if tgt_length is not None else src_length

    expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
    return expanded_mask.expand(batch_size, 1, tgt_length, src_length)


# Copied from transformers.models.bloom.modeling_bloom.build_alibi_tensor
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
    """
    Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it
    relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value
    `softmax(l+a) = softmax(l)`.

    Args:
    Returns tensor shaped (batch_size * num_heads, 1, max_seq_len)
        attention_mask (`torch.Tensor`):
            Token-wise attention mask, this should be of shape (batch_size, max_seq_len).
        num_heads (`int`, *required*):
            number of heads
        dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`):
            dtype of the output tensor
    """
    batch_size, seq_length = attention_mask.shape
    closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
    base = torch.tensor(
        2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
    )
    powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
    slopes = torch.pow(base, powers)

    if closest_power_of_2 != num_heads:
        extra_base = torch.tensor(
            2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
        )
        num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
        extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
        slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)

    # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
    # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
    # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
    # => the query_length dimension will then be broadcasted correctly
    arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
    alibi = slopes[..., None] * arange_tensor
    return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)


class RMSNorm(nn.Module):

    def __init__(self, hidden_size, epsilon=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.epsilon = epsilon

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

        return (self.weight * hidden_states).to(input_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))


class BaichuanAttention(nn.Module):

    def __init__(self, config: "BaichuanConfig"):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.model_max_length

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
            )

        # Layer-wise attention scaling
        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
        self.beta = 1.0

        self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        alibi: torch.Tensor,
        attention_mask: torch.Tensor,
        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, q_len, _ = hidden_states.size()

        proj = self.W_pack(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
        proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
        query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim)
        key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim)
        value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim)

        query_states = query_states.transpose(1, 2).reshape(bsz * self.num_heads, q_len, self.head_dim)
        key_states = key_states.permute(0, 2, 3, 1).reshape(bsz * self.num_heads, self.head_dim, q_len)
        value_states = value_states.transpose(1, 2).reshape(bsz * self.num_heads, q_len, self.head_dim)

        if past_key_value is not None:
            # reuse k, v, self_attention
            past_key, past_value = past_key_value
            key_states = torch.cat([past_key, key_states], dim=2)
            value_states = torch.cat([past_value, value_states], dim=1)

        _, _, kv_seq_len = key_states.shape

        past_key_value = (key_states, value_states) if use_cache else None

        # [batch_size * num_heads, q_length, kv_length]
        # we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11
        matmul_result = alibi.baddbmm(
            batch1=query_states,
            batch2=key_states,
            beta=self.beta,
            alpha=self.inv_norm_factor,
        )

        # change view to [batch_size, num_heads, q_length, kv_length]
        attention_scores = matmul_result.view(bsz, self.num_heads, q_len, kv_seq_len)

        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
        # [batch_size, num_heads, q_length, kv_length]
        input_dtype = attention_scores.dtype
        # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
        if input_dtype == torch.float16:
            attention_scores = attention_scores.to(torch.float)
        attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype)

        # change view [batch_size x num_heads, q_length, kv_length]
        attention_probs_reshaped = attention_probs.view(bsz * self.num_heads, q_len, kv_seq_len)

        # matmul: [batch_size * num_heads, q_length, head_dim]
        attn_output = torch.bmm(attention_probs_reshaped, value_states)

        attn_output = attn_output.view(bsz, self.num_heads, q_len, self.head_dim)

        attn_output = attn_output.transpose(1, 2).reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attention_probs = None

        return attn_output, attention_probs, past_key_value


class BaichuanLayer(nn.Module):

    def __init__(self, config: "BaichuanConfig"):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = BaichuanAttention(config=config)
        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, epsilon=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        alibi: torch.Tensor,
        attention_mask: torch.Tensor,
        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]]]:

        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,
            alibi=alibi,
            attention_mask=attention_mask,
            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 BaichuanPreTrainedModel(PreTrainedModel):
    config_class = BaichuanConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BaichuanLayer"]
    _skip_keys_device_placement = "past_key_values"
    _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, BaichuanModel):
            module.gradient_checkpointing = value

    @staticmethod
    def _convert_to_standard_cache(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        """
        Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
        num_heads, ...]))
        """
        batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
        num_heads = batch_size_times_num_heads // batch_size
        # key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
        # value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
        return tuple(
            (
                layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
                layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
            )
            for layer_past in past_key_value
        )

    @staticmethod
    def _convert_to_baichuan_cache(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        """
        Converts the cache to the format expected by Baichuan, i.e. to tuple(tuple([batch_size * num_heads, ...]))
        """
        batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
        batch_size_times_num_heads = batch_size * num_heads
        # key:  [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
        # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
        return tuple(
            (
                layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
                layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
            )
            for layer_past in past_key_value
        )


class BaichuanModel(BaichuanPreTrainedModel):

    def __init__(self, config: "BaichuanConfig"):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.n_head = config.num_attention_heads

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([BaichuanLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)

        self.gradient_checkpointing = config.gradient_checkpointing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

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

    def build_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
        return build_alibi_tensor(attention_mask, num_heads, dtype)

    def _prepare_attn_mask(
        self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
    ) -> torch.BoolTensor:
        # create causal mask
        # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
        combined_attention_mask = None
        device = attention_mask.device
        _, src_length = input_shape

        if src_length > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape, device=device, past_key_values_length=past_key_values_length
            )

        # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
        expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
        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,
        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

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot provide both input_ids and inputs_embeds simultaneously")
        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 need to provide input_ids or 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[1]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        hidden_states = inputs_embeds

        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
        else:
            attention_mask = attention_mask.to(hidden_states.device)

        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

        # Compute alibi tensor: check build_alibi_tensor documentation
        alibi = self.build_alibi_tensor(attention_mask, self.n_head, dtype=hidden_states.dtype)

        causal_mask = self._prepare_attn_mask(
            attention_mask,
            input_shape=(batch_size, seq_length),
            past_key_values_length=past_key_values_length,
        )

        # 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,
                    alibi,
                    causal_mask,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    alibi=alibi,
                    attention_mask=causal_mask,
                    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 BaichuanForCausalLM(BaichuanPreTrainedModel):

    def __init__(self, config):
        super().__init__(config)
        self.model = BaichuanModel(config)

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

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

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

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

    def get_output_embeddings(self):
        return self.lm_head

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

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

    def get_decoder(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = 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,
        **kwargs
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        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,
            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: torch.LongTensor,
        past_key_values: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs
    ) -> dict:
        if past_key_values:
            input_ids = input_ids[:, -1:]

            # the cache may be in the standard format (e.g. in contrastive search)
            if past_key_values[0][0].shape[0] == input_ids.shape[0]:
                past_key_values = self._convert_to_baichuan_cache(past_key_values)

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

    def _reorder_cache(
        self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.

        Output shares the same memory storage as `past`.
        """
        standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))

        # Get a copy of `beam_idx` on all the devices where we need those indices.
        device_to_beam_idx = {
            past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
        }
        reordered_past = tuple(
            (
                layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
                layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
            )
            for layer_past in standardized_past
        )
        return self._convert_to_baichuan_cache(reordered_past)