model.py

""" PyTorch InternLM2 model."""
import math
import queue
import threading
import warnings
from typing import List, Optional, Tuple, Union
import gc
import json
import os
from collections import OrderedDict

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.distributed as dist
from einops import rearrange
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel, dtype_byte_size
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
)
from megatron.core import parallel_state, tensor_parallel
from deepspeed.pipe import LayerSpec, TiedLayerSpec

from collie.config import CollieConfig
from collie.driver.io import IODriver
from collie.log.logger import logger
from collie.module import (
    ColumnParallelLinearWithoutBias,
    ColumnParallelLMHead,
    RowParallelLinearWithoutBias,
)
from collie.utils import concat_tensor, dict_as_params, env, progress
from collie.models.base import CollieModelForCausalLM
from collie.models.utils import (
    kv_cache_to_inputs_for_layer, inputs_to_kv_cache_for_layer,
    kv_cache_to_inputs_for_model, inputs_to_kv_cache_for_model,
)

try:
    from transformers.generation.streamers import BaseStreamer
except:  # noqa # pylint: disable=bare-except
    BaseStreamer = None

from .configuration_internlm2 import InternLM2Config


_CONFIG_FOR_DOC = "InternLM2Config"

flash_attn_func, flash_attn_varlen_func = None, None
pad_input, index_first_axis, unpad_input = None, None, None
def _import_flash_attn():
    global flash_attn_func, flash_attn_varlen_func
    global pad_input, index_first_axis, unpad_input
    try:
        from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
        from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
    except ImportError:
        raise ImportError("flash_attn is not installed.")

# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


# 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)


# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
class InternLM2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        InternLM2RMSNorm 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)


# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
class InternLM2RotaryEmbedding(nn.Module):
    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):
        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().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # 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=torch.float32)

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


# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    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
        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().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
    """InternLM2RotaryEmbedding 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().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


# Copied from transformers.model.llama.modeling_llama.rotate_half
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)


# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors."""
    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class InternLM2MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        # modified for TP
        self.w1 = ColumnParallelLinearWithoutBias(
            self.hidden_size,
            self.intermediate_size,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.w3 = ColumnParallelLinearWithoutBias(
            self.hidden_size,
            self.intermediate_size,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.w2 = RowParallelLinearWithoutBias(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            input_is_parallel=True,
            init_method=lambda x: x,
        )
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))

        return down_proj


# Copied from transformers.model.llama.modeling_llama.repeat_kv
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)


# Modified from transformers.model.llama.modeling_llama.LlamaAttention
class InternLM2Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: CollieConfig):
        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.is_causal = True

        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.wqkv = ColumnParallelLinearWithoutBias(
            config.hidden_size,
            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )

        self.wo = RowParallelLinearWithoutBias(
            self.num_heads * self.head_dim,
            config.hidden_size,
            bias=False,
            input_is_parallel=True,
            init_method=lambda x: x,
        )
        self._init_rope()
        if self.config.attn_implementation == "flash_attention_2" or self.config.use_flash:
            _import_flash_attn()

    def _init_rope(self):
        if self.config.rope_scaling is None:
            self.rotary_emb = InternLM2RotaryEmbedding(
                self.head_dim,
                max_position_embeddings=self.max_position_embeddings,
                base=self.config.rope_theta,
            )
        else:
            scaling_type = self.config.rope_scaling["type"]
            scaling_factor = self.config.rope_scaling["factor"]
            if scaling_type == "dynamic":
                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    base=self.config.rope_theta,
                    scaling_factor=scaling_factor,
                )
            elif scaling_type == "linear":
                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    base=self.config.rope_theta,
                    scaling_factor=scaling_factor,
                )
            else:
                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
        return self.rotary_emb

    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,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
                "Please make sure use `attention_mask` instead.`"
            )

        bsz, q_len, _ = hidden_states.size()

        qkv_states = self.wqkv(hidden_states)

        qkv_states = rearrange(
            qkv_states,
            "b q (h gs d) -> b q h gs d",
            gs=2 + self.num_key_value_groups,
            d=self.head_dim,
        )

        query_states = qkv_states[..., : self.num_key_value_groups, :]
        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
        key_states = qkv_states[..., -2, :]
        value_states = qkv_states[..., -1, :]

        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
        if self.config.pp_size > 1:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        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

        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/self.config.tp_size, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz, self.num_heads/self.config.tp_size, 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

        # 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/self.config.tp_size, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads/self.config.tp_size, 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, int(self.hidden_size/self.config.tp_size))

        attn_output = self.wo(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
class InternLM2FlashAttention2(InternLM2Attention):
    """
    InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        # InternLM2FlashAttention2 attention does not support output_attentions
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
                "Please make sure use `attention_mask` instead.`"
            )

            # overwrite attention_mask with padding_mask
            attention_mask = kwargs.pop("padding_mask")

        output_attentions = False

        bsz, q_len, _ = hidden_states.size()

        qkv_states = self.wqkv(hidden_states)

        qkv_states = rearrange(
            qkv_states,
            "b q (h gs d) -> b q h gs d",
            gs=2 + self.num_key_value_groups,
            d=self.head_dim,
        )
        query_states = qkv_states[..., : self.num_key_value_groups, :]
        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
        key_states = qkv_states[..., -2, :]
        value_states = qkv_states[..., -1, :]

        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
        if self.config.pp_size > 1:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()
        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

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

        attn_output = self._flash_attention_forward(
            query_states, key_states, value_states, attention_mask, q_len
        )
        attn_output = attn_output.reshape(bsz, q_len, int(self.hidden_size/self.config.tp_size)).contiguous()
        attn_output = self.wo(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    def _flash_attention_forward(
        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`int`, *optional*):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
        """
        # Contains at least one padding token in the sequence
        causal = self.is_causal and query_length != 1
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            attn_output = flash_attn_func(
                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
            )

        return attn_output

    def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )

        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, int(self.num_heads/self.config.tp_size), head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q.to(torch.int64),
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )

INTERNLM2_ATTENTION_CLASSES = {
    "eager": InternLM2Attention,
    "flash_attention_2": InternLM2FlashAttention2,
}

# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
class InternLM2DecoderLayer(nn.Module):
    def __init__(self, config: CollieConfig, layer_idx):
        super().__init__()
        self.hidden_size = config.hidden_size

        # self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
        if config.attn_implementation == "flash_attention_2" or config.use_flash:
            self.attention = InternLM2FlashAttention2(config=config)
        else:
            self.attention = InternLM2Attention(config=config)

        self.feed_forward = InternLM2MLP(config)
        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.config = config
        # add for pp
        self.idx = layer_idx
        # 务必保持变量名一致
        self.use_cache = self.config.model_config.use_cache
        self.hidden_states = None
        self.output_attentions = False

    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,
        **kwargs,
    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            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.attention_norm(hidden_states)

        if position_ids is None:  # for pp
            seq_length = hidden_states.shape[1]
            past_key_values_length = 0
            if past_key_value is not None:
                past_key_values_length = past_key_value[0][0].shape[1]
            device = hidden_states.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)

        # Self Attention
        hidden_states, self_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,
            output_attentions=self.output_attentions,
            use_cache=self.use_cache,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.ffn_norm(hidden_states)
        hidden_states = self.feed_forward(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states, present_key_value

    def forward(self, inputs: dict):
        layer_past = inputs_to_kv_cache_for_layer(idx=self.idx, inputs=inputs)

        if self.config.checkpointing and self.training:
            hidden_states, new_layer_past = torch.utils.checkpoint.checkpoint(
                self._forward,
                inputs["hidden_states"],
                inputs.get("attention_mask", None),
                inputs.get("position_ids", None),
                layer_past,
            )
        else:
            hidden_states, new_layer_past = self._forward(
                inputs["hidden_states"],
                inputs.get("attention_mask", None),
                inputs.get("position_ids", None),
                layer_past
            )  # **inputs
        inputs["hidden_states"] = hidden_states

        inputs.update(kv_cache_to_inputs_for_layer(idx=self.idx, new_layer_past=new_layer_past))
        return inputs


InternLM2_START_DOCSTRING = r"""
    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 ([`InternLM2Config`]):
            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.
"""


# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
@add_start_docstrings(
    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
    InternLM2_START_DOCSTRING,
)
class InternLM2PreTrainedModel(PreTrainedModel):
    config_class = InternLM2Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["InternLM2DecoderLayer"]
    _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_()


InternLM2_INPUTS_DOCSTRING = r"""
    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 `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, decoder_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 `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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.
"""


# Modified from transformers.model.llama.modeling_llama.LlamaModel
@add_start_docstrings(
    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
    InternLM2_START_DOCSTRING,
)
class InternLM2Model(nn.Module):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]

    Args:
        config: InternLM2Config
    """

    _auto_class = "AutoModel"

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

        # self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.tok_embeddings = tensor_parallel.VocabParallelEmbedding(
            config.vocab_size, config.hidden_size
        )
        self.layers = nn.ModuleList([InternLM2DecoderLayer(config, i) for i in range(config.num_hidden_layers)])
        self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

    def get_input_embeddings(self):
        return self.tok_embeddings

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

    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(InternLM2_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[Tuple[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        **kwargs,
    ) -> Union[Tuple, BaseModelOutputWithPast]:

        if self.config.attn_implementation == "flash_attention_2" or self.config.use_flash:
            _import_flash_attn()

        # retrieve input_ids and inputs_embeds
        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:
            batch_size, seq_length = input_ids.shape[:2]
        elif inputs_embeds is not None:
            batch_size, seq_length = inputs_embeds.shape[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        seq_length_with_past = seq_length
        past_key_values_length = 0
        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[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)

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

        if self.config.attn_implementation == "flash_attention_2" or self.config.use_flash:
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        else:
            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
            )

        # embed positions
        hidden_states = inputs_embeds

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

        inputs = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "hidden_states": hidden_states,
            "position_ids": position_ids,
        }

        inputs.update(kv_cache_to_inputs_for_model(past_key_values))

        all_hidden_states = ()
        for layer in self.layers:
            all_hidden_states += (inputs["hidden_states"],)
            inputs.update(layer(inputs))
        inputs["hidden_states"] = self.norm(inputs["hidden_states"])
        all_hidden_states += (inputs["hidden_states"],)

        past_key_values = inputs_to_kv_cache_for_model(self.config.num_hidden_layers, inputs)

        return BaseModelOutputWithPast(
            last_hidden_state=inputs["hidden_states"],
            hidden_states=all_hidden_states,
            past_key_values=past_key_values,
        )

    @classmethod
    def pipeline_layers(cls, config: CollieConfig):
        """
        Get layers of pipeline.

        :return: list
        """
        if isinstance(config, str):
            config = CollieConfig.from_pretrained(config)

        if config.tie_word_embeddings:
            embed_tokens = TiedLayerSpec(
                "embed_tokens",
                dict_as_params(input_keys="input_ids", output_keys="hidden_states"),
                tensor_parallel.VocabParallelEmbedding,
                config.vocab_size,
                config.hidden_size,
            )
        else:
            embed_tokens = LayerSpec(
                dict_as_params(input_keys="input_ids", output_keys="hidden_states"),
                tensor_parallel.VocabParallelEmbedding,
                config.vocab_size,
                config.hidden_size,
            )

        layers = [
            LayerSpec(InternLM2DecoderLayer, config, i) for i in range(config.num_hidden_layers)
        ]
        norm = LayerSpec(
            dict_as_params(input_keys="hidden_states", output_keys="hidden_states"),
            InternLM2RMSNorm,
            hidden_size=config.hidden_size,
            eps=config.rms_norm_eps,
        )

        return [
            ("tok_embeddings", embed_tokens),
            ("layers", layers),
            ("norm", norm),
        ]

# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
class InternLM2ForCausalLM(CollieModelForCausalLM):
    _auto_class = "AutoModelForCausalLM"

    _tied_weights_keys = ["output.weight"]
    base_model_prefix = "model"

    def __init__(self, config):
        super().__init__(config)
        self.model = InternLM2Model(config)
        self.vocab_size = config.vocab_size
        # self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.output = ColumnParallelLinearWithoutBias(
            self.collie_config.hidden_size, self.collie_config.vocab_size, bias=False
        )
        # Initialize weights and apply final processing
        # self.post_init()
        # GenerationMixin 需要的额外参数
        self.config.is_decoder = True
        if config.model_config.tie_word_embeddings:
            self.lm_head.weight = self.embed_tokens.weight
        self.main_input_name = "input_ids"

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

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

    def get_output_embeddings(self):
        return self.output

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

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

    def get_decoder(self):
        return self.model

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.Tensor]] = None,
        **kwargs,
    ):
        output = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            **kwargs,
        )
        logits = self.output(output.last_hidden_state)
        return CausalLMOutputWithPast(
            loss=None,
            logits=logits,
            past_key_values=output.past_key_values,
            hidden_states=output.hidden_states,
            attentions=None,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]

            # Some generation methods already pass only the last input ID
            if input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = input_ids.shape[1] - 1

            input_ids = input_ids[:, remove_prefix_length:]

        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[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

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

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

    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=""):
        if tokenizer.add_bos_token:
            prompt = ""
        else:
            prompt = tokenizer.bos_token
        if meta_instruction:
            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
        for record in history:
            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
        return tokenizer([prompt], return_tensors="pt")

    @torch.no_grad()
    def chat(
        self,
        tokenizer,
        query: str,
        history: List[Tuple[str, str]] = [],
        streamer: Optional[BaseStreamer] = None,
        max_new_tokens: int = 1024,
        do_sample: bool = True,
        temperature: float = 0.8,
        top_p: float = 0.8,
        meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
        "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
        **kwargs,
    ):
        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
        # also add end-of-assistant token in eos token id to avoid unnecessary generation
        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
        outputs = self.generate(
            **inputs,
            streamer=streamer,
            max_new_tokens=max_new_tokens,
            do_sample=do_sample,
            temperature=temperature,
            top_p=top_p,
            eos_token_id=eos_token_id,
            **kwargs,
        )
        outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
        response = tokenizer.decode(outputs, skip_special_tokens=True)
        response = response.split("<|im_end|>")[0]
        history = history + [(query, response)]
        return response, history

    @torch.no_grad()
    def stream_chat(
        self,
        tokenizer,
        query: str,
        history: List[Tuple[str, str]] = [],
        max_new_tokens: int = 1024,
        do_sample: bool = True,
        temperature: float = 0.8,
        top_p: float = 0.8,
        **kwargs,
    ):
        """
        Return a generator in format: (response, history)
        Eg.
        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
        """
        if BaseStreamer is None:
            raise ModuleNotFoundError(
                "The version of `transformers` is too low. Please make sure "
                "that you have installed `transformers>=4.28.0`."
            )

        response_queue = queue.Queue(maxsize=20)

        class ChatStreamer(BaseStreamer):
            def __init__(self, tokenizer) -> None:
                super().__init__()
                self.tokenizer = tokenizer
                self.queue = response_queue
                self.query = query
                self.history = history
                self.response = ""
                self.cache = []
                self.received_inputs = False
                self.queue.put((self.response, history + [(self.query, self.response)]))

            def put(self, value):
                if len(value.shape) > 1 and value.shape[0] > 1:
                    raise ValueError("ChatStreamer only supports batch size 1")
                elif len(value.shape) > 1:
                    value = value[0]

                if not self.received_inputs:
                    # The first received value is input_ids, ignore here
                    self.received_inputs = True
                    return

                self.cache.extend(value.tolist())
                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
                if token.strip() != "<|im_end|>":
                    self.response = self.response + token
                    history = self.history + [(self.query, self.response)]
                    self.queue.put((self.response, history))
                    self.cache = []
                else:
                    self.end()

            def end(self):
                self.queue.put(None)

        def stream_producer():
            return self.chat(
                tokenizer=tokenizer,
                query=query,
                streamer=ChatStreamer(tokenizer=tokenizer),
                history=history,
                max_new_tokens=max_new_tokens,
                do_sample=do_sample,
                temperature=temperature,
                top_p=top_p,
                **kwargs,
            )

        def consumer():
            producer = threading.Thread(target=stream_producer)
            producer.start()
            while True:
                res = response_queue.get()
                if res is None:
                    return
                yield res

        return consumer()

    def clean_cache(self):
        self._clean_hidden_states([*self.model.layers, self.output])
        self._set_use_cache(self.model.layers, False)

    def set_cache(self, use_cache):
        self._set_use_cache(self.model.layers, use_cache)

    @classmethod
    def pipeline_layers(cls, config: CollieConfig):
        """
        Get layers of pipeline.

        :return: list
        """
        if isinstance(config, str):
            config = CollieConfig.from_pretrained(config)

        if config.tie_word_embeddings:
            output = TiedLayerSpec(
                "embed_tokens",
                dict_as_params(input_keys="hidden_states", output_keys="logits"),
                ColumnParallelLMHead,
                config.hidden_size,
                config.vocab_size,
                bias=False,
            )
        else:
            output = LayerSpec(
                dict_as_params(input_keys="hidden_states", output_keys="logits"),
                ColumnParallelLMHead,
                config.hidden_size,
                config.vocab_size,
                bias=False,
            )

        return [("model", InternLM2Model.pipeline_layers(config)), ("output", output)]

    @staticmethod
    def load_parallel_state_dict(
            path: str,
            config: Union[CollieConfig, str],
            process_exclusion: bool = False,
            **kwargs,
    ):
        ...

    @staticmethod
    def load_parallel_state_dict(
            path: str,
            config: Union[CollieConfig, str],
            process_exclusion: bool = False,
            protocol: str = "file",
            **kwargs,
    ):
        """
        Load state_dict from ``path``.

        The format of pretrained model should be the same as that of
        `huggingface`.

        :return: state_dict. Note that the state_dict should be processed
            properly to match the current rank.
        """
        if isinstance(config, str):
            config = CollieConfig.from_pretrained(config)
        io_driver = IODriver.from_protocol(protocol)
        if not io_driver.exists(path):
            raise FileNotFoundError(f"folder {path} not found.")
        state_dict = OrderedDict()
        weights = []
        parts = None
        # 如果开启了进程互斥，那么每个进程都会显示进度条，否则只显示 RANK0 的
        hide_progress = not process_exclusion and int(os.environ.get("RANK", "0")) != 0
        if dist.is_initialized() and process_exclusion:
            # 如果启动了进程互斥，则要进行 dist.get_world_size() 次循环
            rank_order = range(dist.get_world_size())
        else:
            # 不开启只进行一次循环
            rank_order = range(1)
        for rank in rank_order:
            # 如果开启了进程互斥，那么只有对应 RANK 的能进入循环；不开启进程互斥的话就都可以进
            if int(os.environ.get("RANK", "0")) == rank or not process_exclusion:
                # PP 分层的方法保存在了 os.environ["COLLIE_PP_PARTS"], 格式类似于 [0, 17, 35], 左闭右开
                if env.is_pipeline:
                    # 保存的是 json 格式
                    parts = env.pipeline_parts
                if hasattr(config, "num_key_value_heads"):
                    # llama2 (transformers >= 4.31.0)
                    num_key_value_heads = config.num_key_value_heads
                else:
                    num_key_value_heads = config.num_attention_heads
                head_dim = config.hidden_size // config.num_attention_heads
                # 如果存在 .index.json 文件的话，此时不同的 pp 进程可以按需加载自己需要的权重
                # 优先加载 model.safetensors.index.json 文件中的权重
                if io_driver.exists(os.path.join(path, "model.safetensors.index.json")):
                    index_json_file_path = os.path.join(path, "model.safetensors.index.json")
                elif io_driver.exists(os.path.join(path, "pytorch_model.bin.index.json")):
                    index_json_file_path = os.path.join(path, "pytorch_model.bin.index.json")
                else:
                    index_json_file_path = None

                if (
                        index_json_file_path is not None
                        and "COLLIE_PP_PARTS" in os.environ.keys()
                ):
                    weight_map = json.loads(
                        io_driver.load(
                            index_json_file_path, mode="r"
                        )
                    )["weight_map"]
                    # layers 表示自己需要的层
                    layers = env.pipeline_layers_idx
                    # 筛选出形似 model.layers.0 这样的层。包含两个条件：1. 有数字的层；2. 数字加一要在 layers 里面（因为最开始还有个 embedding 占一层）
                    weights.extend(
                        [
                            value
                            for key, value in weight_map.items()
                            if len(key.split(".")) > 2
                               and key.split(".")[2].isdigit()
                               and (int(key.split(".")[2]) + 1) in layers
                        ]
                    )
                    # 去重
                    weights = list(set(weights))
                    # 继续筛选，如果有 0 层，那么就要加载 embedding；如果有最后一层，那么就要加载 lm_head；如果有倒数第二层，那么就要加载 norm
                    if 0 in layers:
                        weights.append(weight_map["model.tok_embeddings.weight"])
                    if max(parts) - 1 in layers:
                        weights.append(weight_map["output.weight"])
                    if max(parts) - 2 in layers:
                        weights.append(weight_map["model.norm.weight"])
                else:
                    # 如果没有 .index.json 文件的话，那么就加载所有的权重
                    # 优先加载 safetensors 存储的权重
                    weights = [
                        weight
                        for weight in io_driver.list(path)
                        if weight.endswith(".safetensors")
                    ]
                    if len(weights) == 0:
                        # 如果没有 safetensors 文件，那么就加载 bin 文件
                        weights = [
                            weight
                            for weight in io_driver.list(path)
                            if weight.endswith(".bin")
                        ]
                with progress(
                    weights,
                    desc="Loading state dict",
                    total=len(weights),
                    disable=hide_progress,
                ) as pbar:
                    for weight in pbar:
                        part_state_dict = io_driver.load(
                            os.path.join(path, weight), mode="rb"
                        )
                        state_dict.update(part_state_dict)
                        del part_state_dict
                if parts is not None:
                    # 这一步是 pp 的复筛
                    layers = env.pipeline_layers_idx
                    for key in list(state_dict.keys()):
                        if key.startswith("layers"):
                            layer = int(key.split(".")[1])
                            if layer + 1 not in layers:
                                state_dict.pop(key)
                        if key.endswith("tok_embeddings.weight"):
                            if 0 not in layers:
                                state_dict.pop(key)
                        if key == "norm.weight":
                            if max(parts) - 2 not in layers:
                                state_dict.pop(key)
                        if key.endswith("output.weight"):
                            if max(parts) - 1 not in layers:
                                state_dict.pop(key)
                # 根据用户配置的新的 tp size 进行分割
                for key in list(state_dict.keys()):
                    col_filter = [
                        "wqkv.weight",
                        "w1.weight",
                        "w3.weight",
                        "tok_embeddings.weight",
                        "output.weight",
                    ]
                    col_split = any([key.endswith(filter) for filter in col_filter])

                    if col_split:
                        tensor = (
                            list(torch.chunk(state_dict[key], config.tp_size, dim=0))[
                                env.tp_rank
                            ]
                            .detach()
                            .clone()
                        )
                        del state_dict[key]
                        if process_exclusion:
                            # CPU 内存回收（速度很慢）
                            gc.collect()
                        state_dict[key] = tensor
                    elif key.endswith("wo.weight") or key.endswith("w2.weight"):
                        tensor = (
                            list(torch.chunk(state_dict[key], config.tp_size, dim=1))[
                                env.tp_rank
                            ]
                            .detach()
                            .clone()
                        )
                        del state_dict[key]
                        if process_exclusion:
                            # CPU 内存回收（速度很慢）
                            gc.collect()
                        state_dict[key] = tensor
            if dist.is_initialized() and process_exclusion:
                # 如果选择了进程互斥，那么本次循环中不需要加载权重的进程需等待
                dist.barrier()
        return state_dict

    @staticmethod
    def save_parallel_state_dict(
            state_dict: dict,
            path: str,
            config: CollieConfig,
            process_exclusion: bool = False,
            **kwargs,
    ):
        ...

    @staticmethod
    def save_parallel_state_dict(
            state_dict: dict,
            path: str,
            config: CollieConfig,
            process_exclusion: bool = False,
            protocol: str = "file",
    ):
        """
        Save state_dict to ``path``.

        The format of saved state dict should be the same as that of
        `huggingface`.
        """
        io_driver = IODriver.from_protocol(protocol)
        # gather to tp rank 0
        if dist.is_initialized() and process_exclusion:
            # 如果启动了进程互斥，则要进行 pp_size 次循环
            rank_order = range(config.pp_size)
        else:
            # 不开启只进行一次循环
            rank_order = range(1)
        dst = parallel_state.get_tensor_model_parallel_src_rank()
        with progress(
                rank_order,
                desc="Saving model",
                disable=int(os.environ.get("RANK", "0")) != 0,
        ) as pbar:
            for rank in pbar:
                if env.dp_rank == 0 and (env.pp_rank == rank or not process_exclusion):
                    for key in sorted(list(state_dict.keys())):
                        tensor_list = None
                        if env.tp_rank == 0:
                            tensor_list = [
                                torch.zeros_like(state_dict[key])
                                .to(state_dict[key].dtype)
                                .cuda()
                                for _ in range(config.tp_size)
                            ]
                        dist.gather(
                            state_dict[key].cuda(),
                            dst=dst,
                            gather_list=tensor_list,
                            group=env.tp_group,
                        )
                        if env.tp_rank == 0:
                            col_filter = [
                                "wqkv.weight",
                                "w1.weight",
                                "w3.weight",
                                "tok_embeddings.weight",
                                "output.weight",
                            ]
                            col_split = any(
                                [key.endswith(filter) for filter in col_filter]
                            )

                            if col_split:
                                state_dict[key] = concat_tensor(tensor_list, dim=0)

                                if process_exclusion:
                                    # CPU 内存回收（速度很慢）
                                    gc.collect()

                            elif key.endswith("wo.weight") or key.endswith("w2.weight"):
                                state_dict[key] = concat_tensor(tensor_list, dim=1)

                                if process_exclusion:
                                    # CPU 内存回收（速度很慢）
                                    gc.collect()

                    if env.tp_rank == 0:
                        # Save gathered weights
                        if env.is_pipeline:
                            ckpt_name = f"pytorch_model-{env.pp_rank + 1:05d}-of-{config.pp_size:05d}.bin"
                            total_size = 0
                            weight_map = {}
                            for name, weight in state_dict.items():
                                weight_size = weight.numel() * dtype_byte_size(
                                    weight.dtype
                                )
                                weight_map[name] = ckpt_name
                                total_size += weight_size
                            index_dict = dict(
                                total_size=total_size, weight_map=weight_map
                            )
                            index_dicts = [None for _ in range(env.pp_size)]
                            dist.gather_object(
                                index_dict, index_dicts if env.pp_rank == 0 else None, group=env.pp_group
                            )
                            if env.pp_rank == 0:
                                total_size = 0
                                weight_map = {}
                                for _index_dict in index_dicts:
                                    total_size += _index_dict["total_size"]
                                    weight_map.update(_index_dict["weight_map"])
                                merged_dict = {
                                    "metadata": {"total_size": total_size},
                                    "weight_map": weight_map,
                                }
                                io_driver.save(
                                    json.dumps(merged_dict, indent=2, sort_keys=True)
                                    + "\n",
                                    os.path.join(path, "pytorch_model.bin.index.json"),
                                )

                        else:
                            ckpt_name = f"pytorch_model.bin"
                        ckpt_path = os.path.join(path, ckpt_name)
                        io_driver.save(state_dict, ckpt_path)
                if dist.is_initialized() and process_exclusion:
                    dist.barrier()
        if env.rank == 0:
            config.save_pretrained(path, protocol=protocol)
        dist.barrier()