modeling_llama.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.
"""PyTorch LLaMA model."""
import math
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (BaseModelOutputWithPast,
                                           CausalLMOutputWithPast,
                                           SequenceClassifierOutputWithPast)
from transformers.modeling_utils import PreTrainedModel
from transformers.models.llama.configuration_llama import LlamaConfig
from transformers.utils import (add_start_docstrings,
                                add_start_docstrings_to_model_forward,
                                replace_return_docstrings)

from lmdeploy.pytorch.modeling.convert_to_qmodules import convert_to_qmodules
from lmdeploy.utils import get_logger

logger = get_logger('lmdeploy')

_CONFIG_FOR_DOC = 'LlamaConfig'


# 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.finfo(dtype).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(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 LlamaRMSNorm(nn.Module):

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

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


class LlamaRotaryEmbedding(torch.nn.Module):
    """RotaryEmbedding for Llama Model.

    This module generates sine and cosine positional encodings based on
    the paper "RoFormer: Enhanced Transformer with Rotary Position Embedding".
    The purpose of this class is to provide positional embeddings to the
    input tensors. It utilizes a cache mechanism to store precomputed
    sine and cosine values for speedup.

    Args:
        dim (int): The dimensionality of the embeddings.
        max_position_embeddings (int, optional): The maximum number of
            position embeddings. Default is 2048.
        base (int, optional): The base value for the inverse frequency
            calculation. Default is 10000.
        device (str, optional): The device to run operations on.
            If None, defaults to the device of the model.
    """

    def __init__(self,
                 dim,
                 max_position_embeddings=2048,
                 base=10000,
                 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).float().to(device) / 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, device, dtype):
        """Sets the cached sine and cosine values for the specified sequence
        length.

        Args:
            seq_len (int): The sequence length for which to set the cache.
            device (str): The device to use for computation.
            dtype (torch.dtype): The data type to be used for tensors.
        """
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached,
                         device=device,
                         dtype=self.inv_freq.dtype)

        freqs = torch.einsum('i,j->ij', 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, seq_len=None):
        """Forward propagation method for the embedding layer. Generates
        positional embeddings for the given input tensor.

        If the sequence length is larger than the cache, it resets the cache.

        Args:
            x (torch.Tensor): Input tensor of shape
                [batch_size, num_attention_heads, seq_len, head_size].
            seq_len (int, optional): Sequence length. If None, it is obtained
                from `x`.

        Returns:
            tuple: Tuple containing cosine and sine positional embeddings.
        """
        # x: [bs, num_attention_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=x.dtype)

        return (
            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
        )


class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """This class extends the `LlamaRotaryEmbedding` with linear scaling.

    It provides a mechanism for adjusting the scale of the positional
    embeddings by dividing the tensor generated by the range of sequence length
    with a scaling factor. This is useful when dealing with sequences of
    varying lengths.

    Credits to Reddit User /u/kaiokendev for this extension.

    Args:
        dim (int): The dimensionality of the embeddings.
        max_position_embeddings (int, optional): The maximum number of
            position embeddings. Default is 2048.
        base (int, optional): The base value for the inverse frequency
            calculation. Default is 10000.
        device (str, optional): The device to run operations on. If None,
            defaults to the device of the model.
        scaling_factor (float, optional): Scaling factor used in adjusting
            the scale of positional embeddings. Default is 1.0.
    """

    def __init__(self,
                 dim,
                 max_position_embeddings=2048,
                 base=10000,
                 device=None,
                 scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        """Sets the cached sine and cosine values for the specified sequence
        length.

        Args:
            seq_len (int): The sequence length for which to set the cache.
            device (str): The device to use for computation.
            dtype (torch.dtype): The data type to use for tensors.
        """
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached,
                         device=device,
                         dtype=self.inv_freq.dtype)
        t = t / self.scaling_factor

        freqs = torch.einsum('i,j->ij', 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)


class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with Dynamic NTK scaling.

    Credits to the Reddit users /u/bloc97 and /u/emozilla
    """

    def __init__(self,
                 dim,
                 max_position_embeddings=2048,
                 base=10000,
                 device=None,
                 scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        if seq_len > self.max_position_embeddings:
            base = self.base * ((self.scaling_factor * seq_len /
                                 self.max_position_embeddings) -
                                (self.scaling_factor - 1))**(self.dim /
                                                             (self.dim - 2))
            inv_freq = 1.0 / (base**(
                torch.arange(0, self.dim, 2).float().to(device) / self.dim))
            self.register_buffer('inv_freq', inv_freq, persistent=False)

        t = torch.arange(self.max_seq_len_cached,
                         device=device,
                         dtype=self.inv_freq.dtype)

        freqs = torch.einsum('i,j->ij', 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 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):
    """Apply rotary positional embeddings to query and key tensors.

    This function applies the cosine and sine positional embeddings on the
    input query (q) and key (k) tensors using element-wise multiplication and
    addition.
    """
    # 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)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 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 LlamaMLP(nn.Module):
    """MLP for Llama Model."""

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

    def forward(self, x):
        if self.config.pretraining_tp > 1:
            slice = self.intermediate_size // self.config.pretraining_tp
            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
            down_proj_slices = self.down_proj.weight.split(slice, dim=1)

            gate_proj = torch.cat([
                F.linear(x, gate_proj_slices[i])
                for i in range(self.config.pretraining_tp)
            ],
                                  dim=-1)
            up_proj = torch.cat([
                F.linear(x, up_proj_slices[i])
                for i in range(self.config.pretraining_tp)
            ],
                                dim=-1)

            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(
                slice, dim=2)
            down_proj = [
                F.linear(intermediate_states[i], down_proj_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            down_proj = sum(down_proj)
        else:
            down_proj = self.down_proj(
                self.act_fn(self.gate_proj(x)) * self.up_proj(x))

        return down_proj


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """This is the equivalent of torch.repeat_interleave(x, dim=1,
    repeats=n_rep).

    The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to
    (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :,
                                  None, :, :].expand(batch,
                                                     num_key_value_heads,
                                                     n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen,
                                 head_dim)


class LlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper."""

    def __init__(self, config: LlamaConfig):
        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.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError('hidden_size must be divisible by num_heads '
                             f'(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=False)
        self.k_proj = nn.Linear(self.hidden_size,
                                self.num_key_value_heads * self.head_dim,
                                bias=False)
        self.v_proj = nn.Linear(self.hidden_size,
                                self.num_key_value_heads * self.head_dim,
                                bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim,
                                self.hidden_size,
                                bias=False)
        self._init_rope()

    def _init_rope(self):
        """Initialize the Rotary Embedding Module."""
        if self.config.rope_scaling is None:
            self.rotary_emb = LlamaRotaryEmbedding(
                self.head_dim,
                max_position_embeddings=self.max_position_embeddings,
                base=self.rope_theta,
            )
        else:
            scaling_type = self.config.rope_scaling['type']
            scaling_factor = self.config.rope_scaling['factor']
            if scaling_type == 'linear':
                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    scaling_factor=scaling_factor,
                    base=self.rope_theta,
                )
            elif scaling_type == 'dynamic':
                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    scaling_factor=scaling_factor,
                    base=self.rope_theta,
                )
            else:
                raise ValueError(f'Unknown RoPE scaling type {scaling_type}')

    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]]]:
        """Forward propagation method for the attention layer."""
        bsz, q_len, _ = hidden_states.size()

        if self.config.pretraining_tp > 1:
            key_value_slicing = (self.num_key_value_heads *
                                 self.head_dim) // self.config.pretraining_tp
            query_slices = self.q_proj.weight.split(
                (self.num_heads * self.head_dim) // self.config.pretraining_tp,
                dim=0)
            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

            query_states = [
                F.linear(hidden_states, query_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            query_states = torch.cat(query_states, dim=-1)

            key_states = [
                F.linear(hidden_states, key_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            key_states = torch.cat(key_states, dim=-1)

            value_states = [
                F.linear(hidden_states, value_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            value_states = torch.cat(value_states, dim=-1)

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

        query_states = query_states.view(bsz, q_len, self.num_heads,
                                         self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
                                     self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
                                         self.head_dim).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)

        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

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        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(
                'Attention weights should be of size '
                f'{(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('Attention mask should be of size '
                                 f'{(bsz, 1, q_len, kv_seq_len)}, '
                                 f'but is {attention_mask.size()}')
            attn_weights = attn_weights + attention_mask

        # 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(
                '`attn_output` should be of size '
                f'{(bsz, self.num_heads, q_len, self.head_dim)}, but is'
                f' {attn_output.size()}')

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

        if self.config.pretraining_tp > 1:
            attn_output = attn_output.split(self.hidden_size //
                                            self.config.pretraining_tp,
                                            dim=2)
            o_proj_slices = self.o_proj.weight.split(
                self.hidden_size // self.config.pretraining_tp, dim=1)
            attn_output = sum([
                F.linear(attn_output[i], o_proj_slices[i])
                for i in range(self.config.pretraining_tp)
            ])
        else:
            attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class LlamaDecoderLayer(nn.Module):
    """Decoder layer for Llama Model."""

    def __init__(self, config: LlamaConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = LlamaAttention(config=config)
        self.mlp = LlamaMLP(config)
        self.input_layernorm = LlamaRMSNorm(config.hidden_size,
                                            eps=config.rms_norm_eps)
        self.post_attention_layernorm = LlamaRMSNorm(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


LLAMA_START_DOCSTRING = r"""    # noqa: E501
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`LlamaConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    'The bare LLaMA Model outputting raw hidden-states without any specific head on top.',  # noqa: E501
    LLAMA_START_DOCSTRING,
)
class LlamaPreTrainedModel(PreTrainedModel):
    config_class = LlamaConfig
    base_model_prefix = 'model'
    supports_gradient_checkpointing = True
    _no_split_modules = ['LlamaDecoderLayer']
    _skip_keys_device_placement = 'past_key_values'

    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, LlamaModel):
            module.gradient_checkpointing = value


LLAMA_INPUTS_DOCSTRING = r"""    # noqa: E501
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential 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)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        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 (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    'The bare LLaMA Model outputting raw hidden-states without any specific head on top.',  # noqa: E501
    LLAMA_START_DOCSTRING,
)
class LlamaModel(LlamaPreTrainedModel):
    """Transformer decoder consisting of *config.num_hidden_layers* layers.
    Each layer is a [`LlamaDecoderLayer`]

    Args:
        config: LlamaConfig
    """

    def __init__(self, config: LlamaConfig):
        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([
            LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)
        ])
        self.norm = LlamaRMSNorm(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    # noqa
    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

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    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, past_key_value,
                                      output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                )
            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 LlamaForCausalLM(LlamaPreTrainedModel):
    """This class extends the `LlamaPreTrainedModel` to enable causal language
    modeling.

    It wraps the basic Llama model (`LlamaModel`) and includes a linear layer
    as a language model head (`lm_head`). The purpose is to predict token
    probabilities, given the previous tokens in the sequence.
    """
    _tied_weights_keys = ['lm_head.weight']

    def __init__(self, config):
        super().__init__(config)
        self.model = LlamaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size,
                                 config.vocab_size,
                                 bias=False)

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

    def get_input_embeddings(self):
        """Get the token embedding layer."""
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        """Set the token embedding layer."""
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        """Get the output embedding layer."""
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        """Set the output embedding layer."""
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        """Set the decoder model."""
        self.model = decoder

    def get_decoder(self):
        """Get the decoder model."""
        return self.model

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast,
                               config_class=_CONFIG_FOR_DOC)
    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""" # noqa: E501
        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, LlamaForCausalLM

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

        >>> prompt = "Hey, are you conscious? 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 conscious? Can you talk to me?\nI'm not conscious, 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) # noqa: E501
        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]
        if self.config.pretraining_tp > 1:
            lm_head_slices = self.lm_head.weight.split(
                self.vocab_size // self.config.pretraining_tp, dim=0)
            logits = [
                F.linear(hidden_states, lm_head_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            logits = torch.cat(logits, dim=-1)
        else:
            logits = self.lm_head(hidden_states)
        logits = logits.float()

        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):
        """Prepare inputs for generating sequences using the model.

        Args:
            input_ids (torch.Tensor): Input token ids.
            past_key_values (list[torch.Tensor], optional): List of past key
                and value states.
            attention_mask (torch.Tensor, optional): Mask indicating which
                tokens should be attended to.
            inputs_embeds (torch.FloatTensor, optional): Optionally,
                the input embeddings instead of token ids.

        Returns:
            dict: Dictionary containing prepared inputs for model generation.
        """
        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):
        """Reorder cached past key-values during generation using beam search.

        This function reorders the cached past key-values according to the
        given indices. It's useful in beam search where the order of hypotheses
        can change from one time-step to another.
        """
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (tuple(
                past_state.index_select(0, beam_idx.to(past_state.device))
                for past_state in layer_past), )
        return reordered_past


@add_start_docstrings(
    """ # noqa: E501
    The LLaMa Model transformer with a sequence classification head on top (linear layer).

    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    LLAMA_START_DOCSTRING,
)
class LlamaForSequenceClassification(LlamaPreTrainedModel):

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = LlamaModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, 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

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    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, SequenceClassifierOutputWithPast]:
        r""" # noqa: E501
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = (return_dict if return_dict is not None else
                       self.config.use_return_dict)

        transformer_outputs = self.model(
            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 = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError(
                'Cannot handle batch sizes > 1 if no padding token is defined.'
            )
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (torch.eq(
                    input_ids, self.config.pad_token_id).long().argmax(-1) -
                                    1).to(logits.device)
            else:
                sequence_lengths = -1

        pooled_logits = logits[torch.arange(batch_size, device=logits.device),
                               sequence_lengths]

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = 'regression'
                elif self.num_labels > 1 and (labels.dtype == torch.long
                                              or labels.dtype == torch.int):
                    self.config.problem_type = 'single_label_classification'
                else:
                    self.config.problem_type = 'multi_label_classification'

            if self.config.problem_type == 'regression':
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == 'single_label_classification':
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels),
                                labels.view(-1))
            elif self.config.problem_type == 'multi_label_classification':
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits, ) + transformer_outputs[1:]
            return ((loss, ) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )