modeling_InternLM.py

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

import rotary_emb
import torch
import torch.utils.checkpoint
import torch.utils.checkpoint
from einops import rearrange
from flash_attn.layers.rotary import ApplyRotaryEmbQKV_ as LegacyApplyRotaryEmbQKV_
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

from .configuration_InternLM_XComposer import InternLMXComposerConfig
from .modeling_utils import LoRALinear

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "InternLMXComposerConfig"


class ApplyRotaryEmbQKV_(torch.autograd.Function):
    """
    ApplyRotaryEmbQKV_
    """
    @staticmethod
    def forward(ctx, qkv, cos, sin, cos_k=None, sin_k=None):
        """
            qkv: (total, 3, nheads, headdim)
            cos, sin: (seqlen, rotary_dim / 2)
            cos_k, sin_k: (seqlen, rotary_dim / 2), optional
        rotary_dim must be <= headdim
        Apply rotary embedding *inplace* to the first rotary_dim of q and k.
        """
        _, three, _, headdim = qkv.shape
        assert three == 3
        rotary_seqlen, rotary_dim = cos.shape
        rotary_dim *= 2
        assert rotary_dim <= headdim
        cos_k = cos if cos_k is None else cos_k
        sin_k = sin if sin_k is None else sin_k
        assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen,
                                                           rotary_dim // 2)
        q1, q2 = qkv[:, 0, :, :rotary_dim].chunk(2, dim=-1)
        rotary_emb.apply_rotary(q1, q2, rearrange(cos, "s d -> s 1 d"),
                                rearrange(sin, "s d -> s 1 d"), q1, q2, False)
        k1, k2 = qkv[:, 1, :, :rotary_dim].chunk(2, dim=-1)
        rotary_emb.apply_rotary(k1, k2, rearrange(cos_k, "s d -> s 1 d"),
                                rearrange(sin_k, "s d -> s 1 d"), k1, k2,
                                False)
        ctx.save_for_backward(cos, sin, cos_k, sin_k)
        return qkv

    @staticmethod
    def backward(ctx, dqkv):
        cos, sin, cos_k, sin_k = ctx.saved_tensors
        rotary_dim = cos.shape[-1]
        rotary_dim *= 2
        dq1, dq2 = dqkv[:, 0, :, :rotary_dim].chunk(2, dim=-1)
        rotary_emb.apply_rotary(dq1, dq2, rearrange(cos, "s d -> s 1 d"),
                                rearrange(sin, "s d -> s 1 d"), dq1, dq2, True)
        dk1, dk2 = dqkv[:, 1, :, :rotary_dim].chunk(2, dim=-1)
        rotary_emb.apply_rotary(dk1, dk2, rearrange(cos_k, "s d -> s 1 d"),
                                rearrange(sin_k, "s d -> s 1 d"), dk1, dk2,
                                True)
        return dqkv, None, None, None, None


class ConvertedInternLMRotaryEmbedding(torch.nn.Module):
    def __init__(self, dim: int, base=10000, scale_base=0, device=None):
        """ """
        super().__init__()
        # Generate and save the inverse frequency buffer (non trainable)
        inv_freq = 1.0 / (base**(
            torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
        self.register_buffer("inv_freq", inv_freq)
        self.scale_base = scale_base
        scale = ((torch.arange(0, dim, 2, device=device, dtype=torch.float32) +
                  0.4 * dim) / (1.4 * dim) if scale_base > 0 else None)
        self.register_buffer("scale", scale)

        self._seq_len_cached = 0
        self._cos_cached = None
        self._sin_cached = None
        self._cos_k_cached = None
        self._sin_k_cached = None

    def _update_cos_sin_cache(self, x, indexes):
        """x: (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim)"""
        if not isinstance(indexes, int):
            seqlen = indexes.max().item() + 1
        else:
            seqlen = indexes + 1  # eval_forward
        # Reset the tables if the sequence length has changed,
        # or if we're on a new device (possibly due to tracing for instance)
        if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
            self._seq_len_cached = seqlen
            t = torch.arange(seqlen,
                             device=x.device,
                             dtype=self.inv_freq.dtype)
            # Don't do einsum, it converts fp32 to fp16
            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
            if self.scale is None:
                self._cos_cached = torch.cos(freqs).to(x.dtype)
                self._sin_cached = torch.sin(freqs).to(x.dtype)
            else:
                power = (torch.arange(
                    seqlen, dtype=self.scale.dtype, device=self.scale.device) -
                         seqlen // 2) / self.scale_base
                scale = self.scale.to(device=power.device)**rearrange(
                    power, "s -> s 1")
                # We want the multiplication by scale to happen in fp32
                self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
                self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
                self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
                self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)

    def forward(self,
                qkv: torch.Tensor,
                indexes=0) -> Tuple[torch.Tensor, torch.Tensor]:
        self._update_cos_sin_cache(qkv, indexes)
        if self.scale is None:
            return apply_rotary_emb_qkv_(qkv, self._cos_cached[indexes],
                                         self._sin_cached[indexes]).to(
                                             qkv.dtype)
        else:
            return apply_rotary_emb_qkv_(
                qkv,
                self._cos_cached[indexes],
                self._sin_cached[indexes],
                self._cos_k_cached[indexes],
                self._sin_k_cached[indexes],
            ).to(qkv.dtype)

    def eval_forward(self, qkv, seqlen_offset=0):
        """
        seqlen_offset: can be used in generation where the qkv being passed in is only the last
        token in the batch.
        """
        self._update_cos_sin_cache(qkv, seqlen_offset + qkv.shape[1])
        if self.scale is None:
            return legacy_apply_rotary_embed_qkv(
                qkv, self._cos_cached[seqlen_offset:],
                self._sin_cached[seqlen_offset:])
        else:
            return legacy_apply_rotary_embed_qkv(
                qkv,
                self._cos_cached[seqlen_offset:],
                self._sin_cached[seqlen_offset:],
                self._cos_k_cached[seqlen_offset:],
                self._sin_k_cached[seqlen_offset:],
            )


apply_rotary_emb_qkv_ = ApplyRotaryEmbQKV_.apply
legacy_apply_rotary_embed_qkv = LegacyApplyRotaryEmbQKV_.apply


class InternConvertedInternLMAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""
    def __init__(self, config: InternLMXComposerConfig):
        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.max_position_embeddings

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads}).")
        self.q_proj = nn.Linear(self.hidden_size,
                                self.num_heads * self.head_dim,
                                bias=config.kqvo_bias)
        self.k_proj = nn.Linear(self.hidden_size,
                                self.num_heads * self.head_dim,
                                bias=config.kqvo_bias)
        self.v_proj = nn.Linear(self.hidden_size,
                                self.num_heads * self.head_dim,
                                bias=config.kqvo_bias)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim,
                                self.hidden_size,
                                bias=config.kqvo_bias)

        self.rotary_emb = ConvertedInternLMRotaryEmbedding(self.head_dim)

    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,
        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, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        q = query_states
        k = key_states
        v = value_states

        qkv = torch.cat([q, k, v], dim=2).contiguous()
        qkv = qkv.view(bsz, q_len, -1)
        qkv = rearrange(qkv,
                        "b s (three h d) -> b s three h d",
                        three=3,
                        d=self.head_dim)

        if past_key_value is not None:
            qkv = self.rotary_emb.eval_forward(
                qkv, seqlen_offset=past_key_value[0].shape[2])
        else:
            qkv = self.rotary_emb.eval_forward(qkv)

        query_states, key_states, value_states = qkv.unbind(2)
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # [bsz, nh, t, hd]

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None

        attn_weights = torch.matmul(query_states, key_states.transpose(
            2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}")

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(
                attn_weights,
                torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights,
                                             dim=-1,
                                             dtype=torch.float32).to(
                                                 query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}")

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

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


# 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 InternLMRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        InternLMRMSNorm 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

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


def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2,
                       gather_indices)
    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2,
                       gather_indices)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class InternLMMLP(nn.Module):
    def __init__(self, hidden_size: int, intermediate_size: int,
                 hidden_act: str, config: InternLMXComposerConfig):
        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 InternLMDecoderLayer(nn.Module):
    def __init__(self, config: InternLMXComposerConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = InternConvertedInternLMAttention(config=config)
        self.mlp = InternLMMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            config=config,
        )
        self.input_layernorm = InternLMRMSNorm(config.hidden_size,
                                               eps=config.rms_norm_eps)
        self.post_attention_layernorm = InternLMRMSNorm(
            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 InternLMPreTrainedModel(PreTrainedModel):
    config_class = InternLMXComposerConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["InternLMDecoderLayer"]
    _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, InternLMModel):
            module.gradient_checkpointing = value


class InternLMModel(InternLMPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLMDecoderLayer`]

    Args:
        config: InternLMXComposerConfig
    """
    def __init__(self, config: InternLMXComposerConfig):
        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([
            InternLMDecoderLayer(config)
            for _ in range(config.num_hidden_layers)
        ])
        self.norm = InternLMRMSNorm(config.hidden_size,
                                    eps=config.rms_norm_eps)

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

    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,
        query_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"
            )

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        if query_embeds is not None:
            inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1)
            batch_size, seq_length, _ = inputs_embeds.shape

        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()

        # 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 InternLMForCausalLM(InternLMPreTrainedModel):

    def __init__(self, config):
        super().__init__(config)
        # TODO: find a way to explicitly initialize InternLM

        if hasattr(config, 'kqvo_bias'):
            setattr(config, 'kqvo_bias', config.kqvo_bias)
        else:
            setattr(config, 'kqvo_bias', False)

        self.model = InternLMModel(config)

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

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

    @classmethod
    def from_pretrained(cls,
                        pretrained_model_name_or_path,
                        llm_cfg=None,
                        *model_args,
                        **kwargs):
        if llm_cfg:
            if 'torch_dtype' in kwargs:
                llm_cfg.torch_dtype = kwargs['torch_dtype']
            if 'load_in_8bit' in kwargs:
                llm_cfg.load_in_8bit = kwargs['load_in_8bit']
            if 'device_map' in kwargs:
                llm_cfg.device_map = kwargs['device_map']
            return cls._from_config(llm_cfg)
        else:
            return super().from_pretrained(pretrained_model_name_or_path,
                                           *model_args, **kwargs)

    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,
        query_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, InternLMForCausalLM

        >>> model = InternLMForCausalLM.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."
        ```"""

        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,
            query_embeds=query_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)
            shift_labels = shift_labels.to(shift_logits.device)

            # Enable model parallelism

            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,
                                      query_embeds=None,
                                      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)
                query_embeds = None

        # 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,
            "query_embeds": query_embeds,
            "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