Delete .ipynb_checkpoints

.ipynb_checkpoints/config-checkpoint.json

@@ -1,54 +0,0 @@
-{
-  "architectures": [
-    "OpenMoeForCausalLM"
-  ],
-  "attention_bias": false,
-  "auto_map": {
-    "AutoModelForCausalLM": "modeling_openmoe.OpenMoeForCausalLM"
-  },
-  "bos_token_id": 0,
-  "dropout_rate": 0.0,
-  "enable_comm_overlap": false,
-  "enable_hierarchical_alltoall": false,
-  "enable_kernel": false,
-  "enable_load_balance": false,
-  "eos_token_id": 1,
-  "expert_parallel": null,
-  "head_dim": 128,
-  "hidden_act": "swiglu",
-  "hidden_size": 3072,
-  "initializer_range": 0.02,
-  "intermediate_size": 12288,
-  "label_smoothing": 0.001,
-  "layer_norm_epsilon": 1e-06,
-  "load_balance_beam_width": 8,
-  "load_balance_group_swap_factor": 0.4,
-  "load_balance_tolerance": 0.1,
-  "max_position_embeddings": 2048,
-  "mlp_gated": true,
-  "model_type": "llama",
-  "moe_layer_interval": 4,
-  "num_attention_heads": 24,
-  "num_experts": 32,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 24,
-  "pad_token_id": 0,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 10000.0,
-  "router_aux_loss_factor": 0.01,
-  "router_capacity_factor_eval": 2.0,
-  "router_capacity_factor_train": 1.25,
-  "router_drop_tks": true,
-  "router_min_capacity": 4,
-  "router_noisy_policy": null,
-  "router_topk": 2,
-  "router_z_loss_factor": 0.0001,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float16",
-  "transformers_version": "4.34.0",
-  "use_cache": true,
-  "vocab_size": 256384,
-  "z_loss_factor": 0.01
-}

.ipynb_checkpoints/modeling_openmoe-checkpoint.py

@@ -1,1146 +0,0 @@
-# coding=utf-8
-# 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 OpenMoE 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 transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.models.llama.configuration_llama import LlamaConfig
-# ========= Disable Flash Attn =============
-# from .llama_attn import LlamaAttention
-# ========= Disable Flash Attn =============
-
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-
-from colossalai.kernel.cuda_native.mha.flash_attn_2 import HAS_FLASH_ATTN
-from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON
-from colossalai.moe.layers import SparseMLP
-from colossalai.moe.manager import MOE_MANAGER
-from colossalai.moe.utils import get_activation, set_moe_args
-
-
-
-if HAS_TRITON:
-    from colossalai.kernel.triton.llama_act_combine_kernel import LlamaActCombine
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-
-def set_openmoe_args(
-    config: LlamaConfig,
-    num_experts: int,
-    moe_layer_interval: int,
-    router_topk: int = 2,
-    router_capacity_factor_train: float = 1.25,
-    router_capacity_factor_eval: float = 2.0,
-    router_min_capacity: int = 4,
-    router_noisy_policy: str = None,
-    router_drop_tks: bool = True,
-    router_aux_loss_factor: float = 0.01,
-    router_z_loss_factor: float = 0.0001,
-    mlp_gated: bool = True,
-    label_smoothing: float = 0.001,
-    z_loss_factor: float = 0.01,
-    enable_load_balance: bool = False,
-    load_balance_tolerance: float = 0.1,
-    load_balance_beam_width: int = 8,
-    load_balance_group_swap_factor: float = 0.4,
-    enable_kernel: bool = False,
-    enable_comm_overlap: bool = False,
-    enable_hierarchical_alltoall: bool = False,
-) -> None:
-    """
-    MoE related arguments.
-    It inserts the MoE arguments into the Llama config.
-
-    Args:
-        config (LlamaConfig): Transformers Llama config.
-        num_experts (int, optional): Number of experts.
-        moe_layer_interval (int, optional): The interval moe layer.
-        router_topk (int, optional): Moe router top k. Defaults to 2.
-        router_capacity_factor_train (float, optional): Moe router max capacity for train. Defaults to 1.25.
-        router_capacity_factor_eval (float, optional): Moe router max capacity for eval. Defaults to 2.0.
-        router_min_capacity (int, optional): Moe router min capacity. Defaults to 4.
-        router_noisy_policy (str, optional): Moe router noisy policy. You can choose [Jitter, Gaussian, None]. Defaults to None.
-        router_drop_tks (bool, optional): Whether moe router drop tokens which exceed max capacity. Defaults to True.
-        router_aux_loss_factor (float, optional): Moe router aux loss. You can refer to STMoE for details. Defaults to 0.01.
-        router_z_loss_factor (float, optional): Moe router z loss. You can refer to STMoE for details. Defaults to 0.01.
-        mlp_gated (bool, optional): Use gate in mlp. Defaults to True.
-        label_smoothing (float, optional): Label smoothing. Defaults to 0.001.
-        z_loss_factor (float, optional): The final outputs' classification z loss factor. Defaults to 0.01.
-        enable_load_balance (bool, optional): Expert load balance. Defaults to False.
-        load_balance_tolerance (float, optional): Expert load balance search's difference tolerance. Defaults to 0.1.
-        load_balance_beam_width (int, optional): Expert load balance search's beam width. Defaults to 8.
-        load_balance_group_swap_factor (float, optional): Expert load balance group swap factor. Longer value encourages less swap. Defaults to 0.4.
-        enable_kernel (bool, optional): Use kernel optimization. Defaults to False.
-        enable_comm_overlap (bool, optional): Use communication overlap for MoE. Recommended to enable for muiti-node training. Defaults to False.
-        enable_hierarchical_alltoall (bool, optional): Use hierarchical alltoall for MoE. Defaults to False.
-    """
-    moe_args = dict(
-        num_experts=num_experts,
-        moe_layer_interval=moe_layer_interval,
-        router_topk=router_topk,
-        router_capacity_factor_train=router_capacity_factor_train,
-        router_capacity_factor_eval=router_capacity_factor_eval,
-        router_min_capacity=router_min_capacity,
-        router_noisy_policy=router_noisy_policy,
-        router_drop_tks=router_drop_tks,
-        router_aux_loss_factor=router_aux_loss_factor,
-        router_z_loss_factor=router_z_loss_factor,
-        mlp_gated=mlp_gated,
-        label_smoothing=label_smoothing,
-        z_loss_factor=z_loss_factor,
-        enable_load_balance=enable_load_balance,
-        load_balance_tolerance=load_balance_tolerance,
-        load_balance_beam_width=load_balance_beam_width,
-        load_balance_group_swap_factor=load_balance_group_swap_factor,
-        enable_kernel=enable_kernel,
-        enable_comm_overlap=enable_comm_overlap,
-        enable_hierarchical_alltoall=enable_hierarchical_alltoall,
-    )
-    set_moe_args(config, moe_args)
-
-
-# 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)
-
-
-def apply_rotary_embedding(q, k, cos, sin, decode=False, rotary_index=None):  
-    # q:  (bs, q_len, num_heads, head_dim)
-    # k:  (bs, q_len [+past_kv_len], num_heads, head_dim)
-    # cos: (max_seq_len, head_dim)
-    # sin: (max_seq_len, head_dim)
-    # rotary_index: (bs, 1)  # only used during decoding, when one query token is input at a time
-    """Helper function to apply Rotary Embeddings."""
-    cos = cos.to(q.dtype)
-    sin = sin.to(q.dtype)
-
-    if len(k.shape) == 3: # for multi query attention
-        k = k.unsqueeze(2)
-        multiquery = True
-    else:
-        multiquery = False
-
-    batch, qlen, qheads, d = q.shape
-    kbatch, klen, kheads, kd = k.shape
-    assert batch == kbatch, f"{batch} != {kbatch}"
-    assert d == kd, f"{d} != {kd}"
-    if decode and qlen == 1 and rotary_index is not None:
-        qcos = cos[rotary_index, :]  # (bs, 1, head_dim)
-        qsin = sin[rotary_index, :]  # (bs, 1, head_dim)
-        qcos = qcos.unsqueeze(2)  # (bs, q_len=1, 1, head_dim)  # broadcast to all heads
-        qsin = qsin.unsqueeze(2)  # (bs, q_len=1, 1, head_dim)    
-    else:
-        qcos, qsin = cos[:qlen, :], sin[:qlen, :]  # (q_len, head_dim)
-        qcos = qcos.unsqueeze(0).unsqueeze(2)  # (1, q_len, 1, head_dim)
-        qsin = qsin.unsqueeze(0).unsqueeze(2)
-    
-    kcos, ksin = cos[:klen, :], sin[:klen, :]  # (k_len, head_dim)
-    kcos = kcos.unsqueeze(0).unsqueeze(2)  # (1, k_len, 1, head_dim)  # broadcast to the whole batch, broadcast to all heads
-    ksin = ksin.unsqueeze(0).unsqueeze(2)  # (1, k_len, 1, head_dim)
-    out_q = (q * qcos) + (rotate_half(q) * qsin)
-    out_k = (k * kcos) + (rotate_half(k) * ksin)
-
-    if multiquery:
-        out_k = out_k.squeeze(2)
-
-    return out_q, out_k
-
-
-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)
-
-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)
-
-def SwiGLU(x):
-    """Gated linear unit activation function.
-    Args:
-        x : input array
-        axis: the axis along which the split should be computed (default: -1)
-    """
-    size = x.shape[-1]
-    assert size % 2 == 0, "axis size must be divisible by 2"
-    x1, x2 = torch.split(x, size // 2, -1)
-    return x1 * (x2 * torch.sigmoid(x2))
-
-
-class OpenMoeMLP(nn.Module):
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.pretraining_tp = config.pretraining_tp
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, 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.hidden_act = config.hidden_act
-        self.act_fn = get_activation(self.hidden_act)
-        self.use_kernel = config.enable_kernel
-
-    def forward(self, x):
-        if self.pretraining_tp > 1:
-            slice = self.intermediate_size // self.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.pretraining_tp)], dim=-1)
-            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.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.pretraining_tp)]
-            down_proj = sum(down_proj)
-        else:
-            if HAS_TRITON and self.use_kernel and self.hidden_act == "swiglu":
-                down_proj = self.down_proj(LlamaActCombine.apply(self.gate_proj(x), self.up_proj(x)))
-            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 OpenMoeAttention(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 = config.head_dim
-        self.num_key_value_heads = config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.pretraining_tp = config.pretraining_tp
-        self.max_position_embeddings = config.max_position_embeddings
-
-        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.generate_fixed_pos_embedding(self.head_dim, self.max_position_embeddings, 1.0, 1e4)
-        self.use_kernel = config.enable_kernel
-        
-
-    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 generate_fixed_pos_embedding(self, features, length, min_timescale=1.0, max_timescale=10000.0):
-        """Generate Sin/Cos for Rotary Embeddings.
-    
-        Args:
-          features: an integer
-          length: an integer
-          min_timescale: an optional float
-          max_timescale: an optional float
-    
-        Returns:
-          output_sin: a float32 Tensor with shape [length, features]
-          output_cos: a float32 Tensor with shape [length, features]
-        """
-        fraction = torch.arange(0, features, 2, dtype=torch.float32) / features
-        timescale = min_timescale * (max_timescale / min_timescale) ** fraction
-        rotational_frequency = 1.0 / timescale
-    
-        sinusoid_inp = torch.einsum("i,j->ij", torch.arange(length, dtype=torch.float32), rotational_frequency)
-    
-        sinusoid_inp = torch.cat([sinusoid_inp, sinusoid_inp], dim=-1)
-
-        self.register_buffer('sin', torch.sin(sinusoid_inp), persistent=False)  # persistent=False --> buffer won't appear in the state_dict
-        self.register_buffer('cos', torch.cos(sinusoid_inp), persistent=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: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        if self.pretraining_tp > 1:
-            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
-            query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.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.pretraining_tp)]
-            query_states = torch.cat(query_states, dim=-1)
-
-            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)]
-            key_states = torch.cat(key_states, dim=-1)
-
-            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.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
-
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        max_length = max(query_states.shape[1], key_states.shape[1])
-        assert max_length <= self.sin.shape[0]
-        sin, cos = self.sin[:max_length], self.cos[:max_length]
-        # TODO: for inference, we can add emb kv into cache to avoid computation
-        query_states, key_states = apply_rotary_embedding(
-            query_states, key_states, cos, sin, decode=True if q_len == 1 else False, rotary_index=position_ids
-        )
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-
-        # 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)
-
-        if HAS_FLASH_ATTN and self.use_kernel:
-            # from flash_attn import flash_attn_func
-            # If we use `from flash_attn import flash_attn_func` directly, 
-            # AutoModelForCausalLM.from_pretrained will treat flash_attn as a compulsory dependency and raise error if it cannot be found.
-            # Here is a workaround to avoid the error.
-            exec("from flash_attn import flash_attn_func")  
-
-            query_states = query_states.transpose(1, 2)
-            key_states = key_states.transpose(1, 2)
-            value_states = value_states.transpose(1, 2)
-            attn_output = flash_attn_func(query_states, key_states, value_states, softmax_scale=1.0, causal=True)
-            attn_output = attn_output.transpose(1, 2).contiguous()
-        else:
-            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
-
-            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                    f" {attn_weights.size()}"
-                )
-
-            if attention_mask is not None:
-                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                    raise ValueError(
-                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                    )
-                if self.training:
-                    attention_mask = attention_mask.clone().detach()
-                attention_mask[:, :, :, 0] = 0
-                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(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)
-
-        if self.pretraining_tp > 1:
-            attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
-            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1)
-            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.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 OpenMoeDecoderLayer(nn.Module):
-    def __init__(self, config: LlamaConfig, moe: bool):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.moe = moe
-        self.self_attn = OpenMoeAttention(config=config)  
-#         self.self_attn = LlamaAttention(config=config)  # TODO: introduce LLaMA Positional Encoding
-        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)
-        if self.moe:
-            self.mlp = SparseMLP(
-                num_experts=config.num_experts,
-                hidden_size=config.hidden_size,
-                intermediate_size=config.intermediate_size,
-                router_top_k=config.router_topk,
-                router_capacity_factor_train=config.router_capacity_factor_train,
-                router_capacity_factor_eval=config.router_capacity_factor_eval,
-                router_min_capacity=config.router_min_capacity,
-                router_noisy_policy=config.router_noisy_policy,
-                router_drop_tks=config.router_drop_tks,
-                mlp_activation=config.hidden_act,
-                mlp_gated=config.mlp_gated,
-                enable_load_balance=config.enable_load_balance,
-                load_balance_tolerance=config.load_balance_tolerance,
-                load_balance_beam_width=config.load_balance_beam_width,
-                load_balance_group_swap_factor=config.load_balance_group_swap_factor,
-                enable_kernel=config.enable_kernel,
-                enable_comm_overlap=config.enable_comm_overlap,
-            )
-            self.pre_extra_mlp_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-            self.extra_mlp = OpenMoeMLP(config)
-        else:
-            self.mlp = OpenMoeMLP(config)
-
-    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
-
-        if self.moe:
-            residual = hidden_states
-            hidden_states = self.pre_extra_mlp_layernorm(hidden_states)
-            hidden_states = self.extra_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"""
-    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.",
-    LLAMA_START_DOCSTRING,
-)
-class OpenMoePreTrainedModel(PreTrainedModel):
-    config_class = LlamaConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["OpenMoeDecoderLayer"]
-    _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, OpenMoeModel):
-            module.gradient_checkpointing = value
-
-
-LLAMA_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 `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.",
-    LLAMA_START_DOCSTRING,
-)
-class OpenMoeModel(OpenMoePreTrainedModel):
-    """
-    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(
-            [
-                OpenMoeDecoderLayer(config, moe=True if (i + 1) % config.moe_layer_interval == 0 else False)
-                for i 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
-    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, output_attentions, None)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-
-class OpenMoeForCausalLM(OpenMoePreTrainedModel):
-    # _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = OpenMoeModel(config)
-        self.pretraining_tp = config.pretraining_tp
-        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()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @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,
-        chunk_head: Optional[bool] = True,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, 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."
-        ```"""
-        # reset moe loss
-        MOE_MANAGER.reset_loss()
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        if self.pretraining_tp > 1:
-            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0)
-            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)]
-            logits = torch.cat(logits, dim=-1)
-
-        loss = None
-        # if no training, just do forward
-        if labels is None:
-            logits = self.lm_head(hidden_states)
-            logits = logits.float()
-        # the vocab size for openmoe is 30w+
-        # which causes great activation memory in training, up to 20G for one sequence
-        # so we use chunk and checkpoint to reduce memory
-        else:
-            if chunk_head == True:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        logits = module(inputs[0])
-                        logits = logits.float()
-                        # Shift so that tokens < n predict n
-                        shift_logits = logits[..., :-1, :].contiguous().float()
-                        shift_labels = inputs[1][..., 1:].contiguous()
-                        # Flatten the tokens
-                        loss = self._calculate_loss(shift_logits, shift_labels)
-                        return loss
-
-                    return custom_forward
-
-                aux_loss, z_loss = self._calculate_router_loss()
-                loss = aux_loss + z_loss
-                for batch_idx in range(hidden_states.shape[0]):
-                    loss = loss + torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(self.lm_head),
-                        hidden_states[batch_idx : batch_idx + 1, :],
-                        labels[batch_idx : batch_idx + 1, :],
-                    )
-                logits = None
-            else:
-                logits = self.lm_head(hidden_states)
-                logits = logits.float()
-                # Shift so that tokens < n predict n
-                shift_logits = logits[..., :-1, :].contiguous()
-                shift_labels = labels[..., 1:].contiguous()
-                # Flatten the tokens
-                aux_loss, z_loss = self._calculate_router_loss()
-                loss = aux_loss + z_loss
-                loss = loss + self._calculate_loss(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
-    ):
-        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):
-        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 _calculate_router_loss(self, aux_loss: list = None, z_loss: list = None):
-        if aux_loss is None or z_loss is None:
-            aux_loss, z_loss = MOE_MANAGER.get_loss()
-        assert len(aux_loss) == len(z_loss) == self.config.num_hidden_layers // self.config.moe_layer_interval
-        aux_loss = self.config.router_aux_loss_factor * sum(aux_loss) / len(aux_loss)
-        z_loss = self.config.router_z_loss_factor * sum(z_loss) / len(z_loss)
-        return aux_loss, z_loss
-
-    def _calculate_loss(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
-        """Compute cross entropy and entropy for log probs and targets.
-
-        Args:
-            logits: [batch, length, num_classes] float array.
-            targets: categorical targets [batch, length] int array.
-
-        Returns:
-            Tuple of scalar loss.
-        """
-        if len(logits.shape) != len(targets.shape) + 1:
-            raise ValueError(
-                "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
-            )
-        vocab_size = logits.shape[-1]
-        confidence = 1.0 - self.config.label_smoothing
-        low_confidence = (1.0 - confidence) / (vocab_size - 1)
-        normalizing_constant = -(
-            confidence * math.log(confidence) + (vocab_size - 1) * low_confidence * math.log(low_confidence + 1e-20)
-        )
-
-        # one hot
-        soft_targets = targets[..., None] == torch.arange(vocab_size, device=targets.device).reshape(
-            (1,) * len(targets.shape) + (-1,)
-        )
-        soft_targets = torch.where(
-            soft_targets, torch.full_like(soft_targets, confidence), torch.full_like(soft_targets, low_confidence)
-        )
-        soft_targets = soft_targets.to(torch.float32)
-
-        # cross entropy
-        total_loss = ZLossCrossEntropy.apply(logits, soft_targets, self.config.z_loss_factor)
-        total_loss = total_loss - normalizing_constant
-        total_loss = torch.mean(torch.sum(total_loss, dim=-1), dim=0)
-        return total_loss
-
-
-class ZLossCrossEntropy(torch.autograd.Function):
-    """Computes cross entropy loss with stable custom gradient.
-
-    Computes a stabilized-gradient version of:
-        -jnp.sum(targets * nn.log_softmax(logits), axis=-1)
-
-    If z_loss > 0, then an auxiliary loss equal to z_loss*log(z)^2
-    will be added to the cross entropy loss (z = softmax normalization constant).
-    The two uses of z_loss are:
-    1. To keep the logits from drifting too far from zero, which can cause
-        unacceptable roundoff errors in bfloat16.
-    2. To encourage the logits to be normalized log-probabilities.
-
-    Args:
-        logits: [batch, length, num_classes] float array.
-        targets: categorical one-hot targets [batch, length, num_classes] float
-        array.
-        z_loss: coefficient for auxilliary z-loss loss term.
-
-    Returns:
-        tuple with the total loss and the z_loss, both
-        float arrays with shape [batch, length].
-    """
-
-    @staticmethod
-    def forward(ctx, logits, targets, z_loss):
-        max_logit = torch.max(logits, dim=-1, keepdim=True)[0]
-        shifted = logits - max_logit
-        exp_shifted = torch.exp(shifted)
-        sum_exp = torch.sum(exp_shifted, axis=-1, keepdims=True)
-        sum_exp_log = torch.log(sum_exp)
-        log_softmax = shifted - sum_exp_log
-        loss = -torch.sum(targets * log_softmax, axis=-1)
-        # Add auxilliary z-loss term.
-        log_z = torch.squeeze(sum_exp_log + max_logit, axis=-1)
-        total_z_loss = z_loss * torch.square(log_z)
-        loss += total_z_loss
-        ctx.z_loss = z_loss
-        ctx.save_for_backward(logits, targets, exp_shifted, sum_exp, log_softmax, log_z)
-        return loss
-
-    @staticmethod
-    def backward(ctx, *grad_outputs):
-        assert len(grad_outputs) == 1
-        g = grad_outputs[0]
-        z_loss = ctx.z_loss
-        logits, targets, exp_shifted, sum_exp, log_softmax, log_z = ctx.saved_tensors
-        # z-loss term adds the (2 * z_loss * log_z) factor.
-        deriv = (1 + 2 * z_loss * log_z).unsqueeze(-1) * exp_shifted / sum_exp - targets
-        g_logits = g.unsqueeze(-1) * deriv
-        g_targets = -g.unsqueeze(-1) * log_softmax
-
-        return (
-            g_logits.to(logits.dtype),
-            g_targets.to(targets.dtype),
-            None,
-        )