models/stabilityai_stablelm-base-alpha-7b-v2/modeling_stablelm_alpha.py

# coding=utf-8
# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. 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.
#
# This code is based off the following work:
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
""" PyTorch StableLM-Alpha model. """
from typing import Optional, Tuple, Union
import math

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

from .configuration_stablelm_alpha import StableLMAlphaConfig


logger = logging.get_logger(__name__)


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

    expanded_mask = mask[:, None, None, :].expand(batch_size, 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 LayerNorm(nn.LayerNorm):
    def __init__(self, normalized_shape: torch.Size, bias: bool = True, **kwargs):
        r"""
        use_cache (`bool`, default = True): whether to use the bias term.
        """
        super().__init__(normalized_shape, **kwargs)
        if not bias:
            self.bias = None


class DecoderLayer(nn.Module):
    def __init__(self, config: StableLMAlphaConfig):
        super().__init__()

        self.norm = LayerNorm(config.hidden_size, eps=config.norm_eps)
        self.attention = Attention(config)
        self.mlp = MLP(config)

    def forward(
        self,
        hidden_states: Optional[torch.FloatTensor],
        attention_mask: Optional[torch.FloatTensor] = 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,
    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        residual = hidden_states

        # Pre-Norm
        hidden_states = self.norm(hidden_states)

        # Self-Attention
        attn_output, attn_weights, present_key_value = self.attention(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )

        # Feed-forward
        mlp_output = self.mlp(hidden_states)

        hidden_states = residual + attn_output + mlp_output

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (attn_weights,)
        if use_cache:
            outputs += (present_key_value,)
        return outputs  # hidden_states, (optional: attn_weights), (optional: present_key_value)


class MLP(nn.Module):
    def __init__(self, config: StableLMAlphaConfig):
        super().__init__()

        hidden_size = config.hidden_size
        multiple_of = 256
        ff_dim = int(8 * hidden_size / 3)
        intermediate_size = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)

        self.gate_proj = torch.nn.Linear(hidden_size, 2 * intermediate_size, bias=False)
        self.out_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
        self.act = nn.SiLU()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        ff, ff_gate = self.gate_proj(x).chunk(2, dim=-1)
        return self.out_proj(ff * self.act(ff_gate))


class RotaryEmbedding(torch.nn.Module):
    def __init__(
        self,
        dim: int,
        max_position_embeddings: int,
        base: int = 10_000,
        device: Optional[torch.device] = None,
    ):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
        # x: [batch_size, num_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
        return (
            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
        )


def rotate_half(x: torch.Tensor):
    """Rotates half the hidden dims of the input."""
    x1, x2 = torch.chunk(x, 2, dim=-1)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class Attention(nn.Module):
    def __init__(self, config: StableLMAlphaConfig):
        super().__init__()

        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.max_position_embeddings
        if self.hidden_size % self.num_heads != 0:
            raise ValueError(
                "`hidden_size` is not divisble by the number of attention heads! Make sure to update them"
            )

        self.qkv_proj = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
        self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self._init_rope()

    def _init_rope(self):
        self.rotary_ndims = int(self.head_dim * self.config.rotary_pct)
        self.rotary_emb = RotaryEmbedding(
            self.rotary_ndims,
            max_position_embeddings=self.config.max_position_embeddings,
            base=self.config.rotary_emb_base,
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        attention_mask: torch.FloatTensor,
        position_ids: torch.LongTensor,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        has_past_key_value = past_key_value is not None

        # Compute QKV
        # [batch_size, seq_len, (num_heads * 3 * head_dim)]
        qkv = self.qkv_proj(hidden_states)

        # [batch_size, seq_len, num_heads, 3 * head_dim]
        new_qkv_shape = qkv.size()[:-1] + (self.num_heads, 3 * self.head_dim)
        qkv = qkv.view(*new_qkv_shape)

        # 3 * [batch_size, num_heads, seq_len, head_dim]
        query = qkv[..., : self.head_dim].permute(0, 2, 1, 3)
        key = qkv[..., self.head_dim:(2 * self.head_dim)].permute(0, 2, 1, 3)
        value = qkv[..., (2 * self.head_dim):].permute(0, 2, 1, 3)

        # Compute rotary embeddings on rotary_ndims
        # [batch_size, num_heads, seq_len, rotary_ndims]
        query_rot = query[..., :self.rotary_ndims]
        query_pass = query[..., self.rotary_ndims:]
        key_rot = key[..., :self.rotary_ndims]
        key_pass = key[..., self.rotary_ndims:]

        # Compute token offset for rotary embeddings (when decoding)
        kv_seq_len = key.shape[-2]
        if has_past_key_value:
            kv_seq_len += past_key_value[0].shape[-2]

        # Add rotary embeddings to query and key
        cos, sin = self.rotary_emb(value, seq_len=kv_seq_len)
        query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)

        # Concatenate rotary embeddings with pass-through query and key
        # [batch_size, num_heads, seq_len, head_dim]
        query = torch.cat((query, query_pass), dim=-1)
        key = torch.cat((key, key_pass), dim=-1)

        # Reuse past key-value states
        if has_past_key_value:
            key = torch.cat((past_key_value[0], key), dim=2)
            value = torch.cat((past_key_value[1], value), dim=2)
        present_key_value = (key, value) if use_cache else None

        # [batch_size, num_heads, seq_len, head_dim]
        query = query.transpose(1, 2).contiguous()
        key = key.transpose(1, 2).contiguous()
        value = value.transpose(1, 2).contiguous()

        # Compute attention
        softmax_scale = 1 / math.sqrt(self.head_dim)
        attn_scores = torch.einsum('bthd,bshd->bhts', query, key * softmax_scale)
        # Apply the attention mask
        if attention_mask is not None:
            attn_scores = attn_scores + attention_mask
        attn_weights = nn.functional.softmax(attn_scores, dim=-1, dtype=torch.float32).to(query.dtype)
        attn_output = torch.einsum('bhts,bshd->bthd', attn_weights, value)

        # Merge heads
        attn_output = attn_output.reshape(attn_output.shape[0], attn_output.shape[1], -1)

        # Final linear projection
        attn_output = self.out_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, present_key_value


def attention_mask_func(attention_scores: torch.Tensor, ltor_mask: torch.Tensor):
    attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min)
    return attention_scores


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

    config_class = StableLMAlphaConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DecoderLayer"]
    _skip_keys_device_placement = "past_key_values"

    def _init_weights(self, module: nn.Module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
        if isinstance(module, StableLMAlphaModel):
            module.gradient_checkpointing = value


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."""
    batch_size, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, 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(batch_size, 1, tgt_len, tgt_len + past_key_values_length)


class StableLMAlphaModel(StableLMAlphaPreTrainedModel):
    def __init__(self, config: StableLMAlphaConfig):
        super().__init__(config)
        self.config = config

        self.embed = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.final_norm = LayerNorm(config.hidden_size, eps=config.norm_eps)

        self.gradient_checkpointing = False
        self.post_init()

    def get_input_embeddings(self):
        return self.embed

    def set_input_embeddings(self, value: nn.Module):
        self.embed = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(
        self,
        attention_mask: torch.Tensor,
        input_shape: torch.Size,
        inputs_embeds: torch.Tensor,
        past_key_values_length: int,
    ):
        # Create causal mask
        # [batch_size, seq_len] -> [batch_size, 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:
            # [batch_size, seq_len] -> [batch_size, 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: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Tuple[Tuple[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]:
        r"""
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers`
            with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks.
            Can be used to speed up decoding. If `past_key_values` are used, the user
            can optionally input only the last `decoder_input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)`
            instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        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`).
        """
        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
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        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 not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        batch_size, seq_length = input_shape

        if past_key_values is None:
            past_key_values_length = 0
            past_key_values = tuple([None] * self.config.num_hidden_layers)
            seq_length_with_past = seq_length
        else:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length + 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(input_ids)

        # Attention mask.
        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(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        present_key_values = () if use_cache else None

        for _, (decoder_layer, past_key_value) in enumerate(zip(self.layers, past_key_values)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:

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

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                    # `None` for `past_key_value`
                    None,
                )
            else:
                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 = outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (outputs[1],)

            if use_cache:
                present_key_values += (outputs[2 if output_attentions else 1],)

        hidden_states = self.final_norm(hidden_states)

        # Add last hidden state
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        present_key_values = present_key_values if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=present_key_values,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
        )


class StableLMAlphaForCausalLM(StableLMAlphaPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: StableLMAlphaConfig):
        super().__init__(config)

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

        self.post_init()

    def get_output_embeddings(self):
        return self.lm_head

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

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Tuple[Tuple[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"""
        Example:

        ```python
        >>> from transformers import AutoTokenizer, StableLMAlphaForCausalLM, StableLMAlphaConfig
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2", trust_remote_code=True)
        >>> config = StableLMAlphaConfig.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2")
        >>> config.is_decoder = True
        >>> model = StableLMAlphaForCausalLM.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2", config=config)

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> logits = outputs.logits
        ```
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            past_key_values=past_key_values,
            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)

        lm_loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shift_logits = logits[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))

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

        return CausalLMOutputWithPast(
            loss=lm_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: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs
    ):
        # Cut decoder_input_ids if past is used
        if past_key_values and past_key_values[0] is not None:
            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(
            {
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
        )

        return model_inputs

    def _reorder_cache(self, past_key_values: torch.Tensor, beam_idx: int):
        reordered_past = ()
        for past_key_value in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx) for past_state in past_key_value[:2]) + past_key_value[2:],
            )
        return reordered_past


StableLMAlphaConfig.register_for_auto_class()
StableLMAlphaForCausalLM.register_for_auto_class("AutoModelForCausalLM")