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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- MoE
+---
+# LLaMA-MoE-v1-3.5B (4/16) SFT
+
+[[💻 Code]](https://github.com/pjlab-sys4nlp/llama-moe) | [[📜 Technical Report]](https://github.com/pjlab-sys4nlp/llama-moe/blob/main/docs/LLaMA_MoE.pdf)
+
+This is the supervised fine-tuned version of [LLaMA-MoE-v1-3_5B-4_16](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-4_16) on [Deita-6k](https://huggingface.co/datasets/hkust-nlp/deita-6k-v0) for 2 epochs.
+
+
+| Model                     | \#Activated Experts | \#Experts | \#Activated Params |                         Foundation Model                          |                              SFT Model                               |
+| :------------------------ | :-----------------: | :-------: | :----------------: | :---------------------------------------------------------------: | :------------------------------------------------------------------: |
+| **LLaMA-MoE-3.0B**        |          2          |    16     |        3.0B        | [🤗 base](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_0B-2_16) | [🤗 SFT](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_0B-2_16-sft) |
+| **LLaMA-MoE-3.5B (4/16)** |          4          |    16     |        3.5B        | [🤗 base](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-4_16) | [🤗 SFT](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-4_16-sft) |
+| **LLaMA-MoE-3.5B (2/8)**  |          2          |     8     |        3.5B        | [🤗 base](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-2_8)  | [🤗 SFT](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-2_8-sft)  |
+
+
+## 🚀 QuickStart
+
+```python
+# python>=3.10
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_dir = "llama-moe/LLaMA-MoE-v1-3_5B-4_16-sft"
+tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, trust_remote_code=True)
+model.eval()
+model.cuda()
+
+input_text = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. human: Give me a three-day plan in Suzhou. gpt:"
+inputs = tokenizer(input_text, return_tensors="pt")
+input_ids = inputs["input_ids"].cuda()
+
+pred = model.generate(input_ids, max_length=100, temperature=1.0, do_sample=True, use_cache=True)
+print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
+"""
+Sure, I can provide you with a three-day itinerary in Suzhou. Here's what we can do:
+
+Day 1:
+
+* Visit Suzhou Industrial Park, a major commercial and manufacturing district ...
+"""
+```
+
+## 📊 Performance
+
+| Model                                  | MMLU  | ARC-c | HellaSeag | TruthfulQA | MT-Bench |
+| :------------------------------------- | :---: | :---: | :-------: | :--------: | :------: |
+| Sheared LLaMA-2.7B ShareGPT            | 28.41 | 41.04 |   71.21   |   47.65    |   3.79   |
+| Sheared LLaMA-2.7B Deita6K (Our Impl.) | 25.24 | 43.69 |   71.70   |   49.00    |   4.06   |
+| LLaMA-MoE-v1-3.0B (2/16)               | 23.61 | 43.43 |   72.28   |   44.24    |   4.15   |
+| LLaMA-MoE-v1-3.5B (4/16)               | 26.49 | 48.29 |   75.10   |   45.91    |   4.60   |
+| LLaMA-MoE-v1-3.5B (2/8)                | 25.53 | 45.99 |   74.95   |   44.39    |   4.72   |
+
+## 📃 Citation
+
+```bibtex
+@misc{llama-moe2023,
+  title={LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training},
+  author={LLaMA-MoE Team},
+  year={2023},
+  publisher={Dec}
+}
+```

config.json

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+{
+  "_name_or_path": "/mnt/petrelfs/zhutong/llama-moe-models/LLaMA-MoE-v1-3_5B-4_16-new",
+  "add_weight_norm": false,
+  "architectures": [
+    "LlamaMoEForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_llama_moe.LlamaMoEConfig",
+    "AutoModel": "modeling_llama_moe_hf.LlamaMoEModel",
+    "AutoModelForCausalLM": "modeling_llama_moe_hf.LlamaMoEForCausalLM"
+  },
+  "bos_token_id": 1,
+  "calculator_type": "UniversalCalculator",
+  "capacity_factor": 1.25,
+  "drop_tokens": true,
+  "dropped_padding": "zero",
+  "eos_token_id": 2,
+  "gate_add_noise": true,
+  "gate_balance_loss_weight": 0.01,
+  "gate_network": "mlp",
+  "gate_noise_epsilon": 0.01,
+  "gate_type": "TopKBalancedNoisyGate",
+  "gate_use_balance": true,
+  "gate_use_softmax": true,
+  "gates": "mlp",
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama_moe",
+  "multiply_gate_scores": true,
+  "num_attention_heads": 32,
+  "num_experts": 16,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "num_selects": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "score_scale_factor": 4.0,
+  "size_experts": [
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+  ],
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration_llama_moe.py

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+from transformers.configuration_utils import PretrainedConfig
+
+
+class LlamaMoEConfig(PretrainedConfig):
+    model_type = "llama_moe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        # -------- moe expert configs --------
+        num_experts=16,
+        num_selects=4,
+        size_experts=None,
+        # -------- moe gate configs --------
+        gate_type="TopKBalancedNoisyGate",
+        gate_network="mlp",
+        gate_use_softmax=True,
+        gate_use_balance=True,
+        gate_balance_loss_weight=1e-2,
+        gate_add_noise=True,
+        # TopKBalancedNoisyGate
+        gate_noise_epsilon=1e-2,
+        # -------- moe calculator configs --------
+        calculator_type="UniversalCalculator",
+        multiply_gate_scores=True,
+        score_scale_factor=1.0,
+        add_weight_norm=False,
+        # SwitchDropTokenCalculator
+        drop_tokens=True,
+        dropped_padding="zero",
+        capacity_factor=1.25,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        self.num_experts = num_experts
+        self.num_selects = num_selects
+        self.size_experts = size_experts
+
+        self.gate_type = gate_type
+        self.gate_network = gate_network
+        self.gate_use_softmax = gate_use_softmax
+        self.gate_use_balance = gate_use_balance
+        self.gate_balance_loss_weight = gate_balance_loss_weight
+        self.gate_add_noise = gate_add_noise
+        self.gate_noise_epsilon = gate_noise_epsilon
+
+        self.calculator_type = calculator_type
+        self.multiply_gate_scores = multiply_gate_scores
+        self.score_scale_factor = score_scale_factor
+        self.add_weight_norm = add_weight_norm
+        self.drop_tokens = drop_tokens
+        self.dropped_padding = dropped_padding
+        self.capacity_factor = capacity_factor
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"
+            )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.36.2"
+}

model-00001-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3b9d76d82f9f5d6f8d03da589fae79a95b12c03f43ef644f4b068c68913b70b
+size 4998589368

model-00002-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0e4e04523c8457023d7f10c4865dd3c0ab56a768b5f118ae82826c6c88e9e2f
+size 4984439672

model-00003-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f89bd5d2e817eaa4ff8eb6d4222189dbaaa1962173bb652f7d426a0e7b31c1d
+size 3502447520

modeling_llama_moe_hf.py

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+import math
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+import torch.utils.checkpoint
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions.normal import Normal
+from transformers.modeling_outputs import (
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.utils import ModelOutput, logging
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+    _prepare_4d_attention_mask,
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10
+
+from .configuration_llama_moe import LlamaMoEConfig
+
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+
+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,
+    )
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaMoEConfig"
+
+
+@dataclass
+class CalculatorOutput(ModelOutput):
+    hidden_states: Optional[torch.FloatTensor] = None
+    num_dropped_tokens: Optional[int] = None
+
+
+@dataclass
+class BaseMoEModelOutputWithPast(ModelOutput):
+    """
+    Args:
+        num_dropped_tokens: layer idx to the number of dropped tokens
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    balance_loss: Optional[float] = None
+    num_dropped_tokens: Optional[Tuple[torch.Tensor]] = None
+    gate_load: Optional[Tuple[list]] = None
+    gate_importance: Optional[Tuple[list]] = None
+
+
+@dataclass
+class MoECausalLMOutputWithPast(CausalLMOutputWithPast):
+    balance_loss: Optional[float] = None
+    num_dropped_tokens: Optional[Tuple[int]] = None
+    gate_load: Optional[Tuple[list[torch.Tensor]]] = None
+    gate_importance: Optional[Tuple[list[torch.Tensor]]] = None
+
+
+@dataclass
+class MoEMlpOutput(ModelOutput):
+    hidden_states: Optional[torch.FloatTensor] = None
+    balance_loss: Optional[torch.FloatTensor] = None
+    num_dropped_tokens: Optional[int] = None
+    gate_load: Optional[list] = None
+    gate_importance: Optional[list] = None
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    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)
+
+        # 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: LlamaMoEConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        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.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = 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()
+
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        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:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        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.hidden_size)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaFlashAttention2(LlamaAttention):
+    """
+    Llama flash attention module. This module inherits from `LlamaAttention` 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 __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # LlamaFlashAttention2 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()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        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.get_usable_length(kv_seq_len, self.layer_idx)
+        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:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (LlamaRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(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)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        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._upad_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 _upad_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, self.num_heads, 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,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+class LlamaSdpaAttention(LlamaAttention):
+    """
+    Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from LlamaAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                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,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        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.get_usable_length(kv_seq_len, self.layer_idx)
+        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:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        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()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+LLAMA_ATTENTION_CLASSES = {
+    "eager": LlamaAttention,
+    "flash_attention_2": LlamaFlashAttention2,
+    "sdpa": LlamaSdpaAttention,
+}
+
+
+class TopKBalancedNoisyGate(nn.Module):
+    def __init__(
+        self,
+        input_size,
+        num_experts,
+        num_selects,
+        gate_network="mlp",
+        use_softmax=True,
+        use_balance=True,
+        balance_loss_weight=1e-2,
+        add_noise=True,
+        noise_epsilon=1e-2,
+    ):
+        super(TopKBalancedNoisyGate, self).__init__()
+        assert num_selects <= num_experts
+        self.input_size = input_size
+        self.num_experts = num_experts
+        self.num_selects = num_selects
+
+        self.gate_network_type = gate_network
+        self.gate_network = self.get_gate_network(gate_network, input_size, num_experts)
+
+        self.use_softmax = use_softmax
+        self.softmax = nn.Softmax(1)
+
+        self.use_balance = use_balance
+        self.balance_loss_weight = balance_loss_weight
+
+        # add_noise
+        self.add_noise = add_noise
+        self.noise_epsilon = noise_epsilon
+        self.warned = False
+        if self.add_noise:
+            self.weight_noise = nn.Linear(input_size, num_experts, bias=False)
+            self.weight_noise.weight.data = torch.zeros(
+                (num_experts, input_size),
+                requires_grad=True,
+                device=self.weight_noise.weight.data.device,
+                dtype=self.weight_noise.weight.data.dtype,
+            )
+            self.mean = 0.0
+            self.std = 1.0
+            self.normal = Normal(self.mean, self.std)
+            self.softplus = nn.Softplus()
+
+        self.reset_parameters()
+
+    def get_gate_network(self, gate_type, input_size, num_experts):
+        gate_type = gate_type.lower()
+
+        if gate_type == "linear":
+            gate_network = nn.Linear(input_size, num_experts, bias=False)
+            nn.init.zeros_(gate_network.weight)
+        elif gate_type == "mlp":
+            gate_network = torch.nn.Sequential(
+                torch.nn.Linear(input_size, num_experts, bias=False),
+                torch.nn.Tanh(),
+                torch.nn.Linear(num_experts, num_experts, bias=False),
+            )
+        else:
+            raise ValueError(f'Unexpected gate_type: {gate_type}.')
+
+        return gate_network
+
+    def reset_gate_network(self):
+        if "gate_network_type" not in vars(self):
+            raise KeyError(f"{type(self)} does not have a gate network.")
+        else:
+            self.gate_network = self.get_gate_network(
+                self.gate_network_type, self.input_size, self.num_experts
+            )
+
+    def reset_parameters(self):
+        if self.add_noise:
+            nn.init.zeros_(self.weight_noise.weight)
+            # nn.init.zeros_(self.weight_noise)
+
+    def cv_squared(self, x, eps=1e-10):
+        """The squared coefficient of variation of a sample.
+        Useful as a loss to encourage a positive distribution to be more uniform.
+        Epsilons added for numerical stability.
+        Returns 0 for an empty Tensor.
+        Args:
+        x: a `Tensor`.
+        Returns:
+        a `Scalar`.s
+        """
+        if x.shape[0] == 1:
+            return torch.tensor(0.0, device=x.device)
+        return x.float().var() / (x.float().mean() ** 2 + eps)
+
+    def forward(self, x):
+        logits_gate = self.gate_network(x)
+        if self.training and self.add_noise:
+            noise_mm = self.weight_noise(x)
+            noise_control = self.softplus(noise_mm) + self.noise_epsilon
+            logits_noise = torch.randn_like(logits_gate) * noise_control
+            logits = logits_gate + logits_noise
+        else:
+            logits = logits_gate
+
+        top_logits, top_indices = logits.topk(min(self.num_selects + 1, self.num_experts), dim=1)  # 选择并排序前k+1个权重
+        top_k_logits = top_logits[:, :self.num_selects]
+        top_k_indices = top_indices[:, :self.num_selects]
+        top_k_scores = self.softmax(top_k_logits.to(torch.float32)) if self.use_softmax else top_k_logits
+        top_k_scores = top_k_scores.to(logits.dtype)
+
+        zeros = torch.zeros_like(logits, requires_grad=True, device=logits.device)
+        scores_filtered = zeros.scatter(dim=1, index=top_k_indices, src=top_k_scores)  # shape(batch_size, num_experts)
+        importance = scores_filtered.sum(0)  # shape(num_experts)
+
+        if self.training:
+            if self.add_noise and self.num_selects != self.num_experts:
+                batch_size = top_logits.size(0)
+                m = top_logits.size(1)
+                top_values_flat = top_logits.flatten()
+                threshold_positions_if_in = torch.arange(batch_size, device=x.device) * m + self.num_selects
+                threshold_if_in = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_in), 1)
+                is_in = torch.gt(logits_noise, threshold_if_in)
+                threshold_positions_if_out = threshold_positions_if_in - 1
+                threshold_if_out = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_out), 1)
+                # is each value currently in the top k.
+                prob_if_in = self.normal.cdf((logits_gate - threshold_if_in) / noise_control)
+                prob_if_out = self.normal.cdf((logits_gate - threshold_if_out) / noise_control)
+                prob = torch.where(is_in, prob_if_in, prob_if_out)
+                load = prob.sum(0)
+            else:
+                load = (scores_filtered > 0).sum(0)
+                if not self.add_noise and not self.warned:
+                    warnings.warn('Gradient-trackable implementation for load calculation is only available when "add_noise=True". '
+                                  'Training without noise will block the gradient from "load" path and lead to inconsistency in optimization objectives.')
+                    self.warned = True
+        else:
+            load = (scores_filtered > 0).sum(0)
+
+        if self.use_balance:
+            balance_loss = self.cv_squared(importance) + self.cv_squared(load)
+            balance_loss *= self.balance_loss_weight
+        else:
+            balance_loss = torch.tensor(-100.0, device=x.device)
+
+        return {
+            "topK_indices": top_k_indices,
+            "topK_scores": top_k_scores,
+            "balance_loss": balance_loss,
+            "load": load,
+            "importance": importance,
+        }
+
+
+class LinearGLUExperts(nn.Module):
+    """
+    Modified from transformers.models.llama.modeling_llama.LlamaMLP
+    """
+
+    __constants__ = [
+        "bias",
+        "in_features",
+        "hidden_features",
+        "out_features",
+        "hidden_act",
+        "num_experts",
+        "size_experts",
+    ]
+
+    def __init__(
+        self,
+        in_features,
+        hidden_features,
+        out_features,
+        hidden_act,
+        num_experts,
+        size_experts=None,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(LinearGLUExperts, self).__init__()
+        self.in_features = in_features
+        self.hidden_features = hidden_features
+        self.out_features = out_features
+        self.hidden_act = hidden_act
+        self.num_experts = num_experts
+
+        if size_experts is None:
+            # all experts share the same number of hidden neurons
+            assert hidden_features % num_experts == 0
+            size_per_expert = hidden_features // num_experts
+            size_experts = [size_per_expert for _ in range(num_experts)]
+        else:
+            # use specified expert sizes
+            assert (
+                len(size_experts) == num_experts
+                and sum(size_experts) == hidden_features
+            )
+        self.size_experts = size_experts
+
+        self.act_fn = ACT2FN[hidden_act]
+
+        self.weight_gate = nn.ParameterList()
+        self.weight_up = nn.ParameterList()
+        self.weight_down = nn.ParameterList()
+
+        for i in range(num_experts):
+            # this matrix will be transposed when performing linear forwarding
+            this_expert_weight_gate = nn.Parameter(
+                torch.empty((size_experts[i], in_features), **factory_kwargs)
+            )
+            # this matrix will be transposed when performing linear forwarding
+            this_expert_weight_up = nn.Parameter(
+                torch.empty((size_experts[i], in_features), **factory_kwargs)
+            )
+            # this matrix will be transposed when performing linear forwarding
+            this_expert_weight_down = nn.Parameter(
+                torch.empty((out_features, size_experts[i]), **factory_kwargs)
+            )
+            self.weight_gate.append(this_expert_weight_gate)
+            self.weight_up.append(this_expert_weight_up)
+            self.weight_down.append(this_expert_weight_down)
+
+        if bias:
+            self.bias_gate = nn.ParameterList()
+            self.bias_up = nn.ParameterList()
+            self.bias_down = nn.ParameterList()
+
+            for i in range(num_experts):
+                this_expert_bias_gate = nn.Parameter(
+                    torch.empty((size_experts[i],), **factory_kwargs)
+                )
+                this_expert_bias_up = nn.Parameter(
+                    torch.empty((size_experts[i],), **factory_kwargs)
+                )
+                this_expert_bias_down = nn.Parameter(
+                    torch.empty((out_features,), **factory_kwargs)
+                )
+                self.bias_gate.append(this_expert_bias_gate)
+                self.bias_up.append(this_expert_bias_up)
+                self.bias_down.append(this_expert_bias_down)
+        else:
+            self.register_parameter("bias_gate", None)
+            self.register_parameter("bias_up", None)
+            self.register_parameter("bias_down", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for i in range(self.num_experts):
+            nn.init.kaiming_uniform_(self.weight_gate[i], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.weight_up[i], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.weight_down[i], a=math.sqrt(5))
+            if self.bias_gate is not None:
+                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_gate[i])
+                bound = 1 / math.sqrt(fan_in)
+                nn.init.uniform_(self.bias_gate[i], -bound, bound)
+            if self.bias_up is not None:
+                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_up[i])
+                bound = 1 / math.sqrt(fan_in)
+                nn.init.uniform_(self.bias_up[i], -bound, bound)
+            if self.bias_down is not None:
+                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_down[i])
+                bound = 1 / math.sqrt(fan_in)
+                nn.init.uniform_(self.bias_down[i], -bound, bound)
+
+    def forward(self, input, i):
+        gate = self.act_fn(
+            F.linear(
+                input,
+                self.weight_gate[i],
+                self.bias_gate[i] if self.bias_gate is not None else None,
+            )
+        )
+        up = F.linear(
+            input,
+            self.weight_up[i],
+            self.bias_up[i] if self.bias_up is not None else None,
+        )
+        down = F.linear(
+            gate * up,
+            self.weight_down[i],
+            self.bias_down[i] if self.bias_down is not None else None,
+        )
+        return down
+
+    def extra_repr(self):
+        return (
+            "in_features={}, hidden_features={}, out_features={}, hidden_act={},"
+            " num_experts={}, size_experts={}, bias={}".format(
+                self.in_features,
+                self.hidden_features,
+                self.out_features,
+                self.hidden_act,
+                self.num_experts,
+                self.size_experts,
+                self.bias_gate is not None,
+            )
+        )
+
+
+class UniversalCalculator(nn.Module):
+    def __init__(
+        self,
+        experts: LinearGLUExperts,
+        multiply_gate_scores=True,
+        score_scale_factor=1.0,
+        add_weight_norm: bool = False,
+    ):
+        super(UniversalCalculator, self).__init__()
+        self.experts = experts
+        # TODO (zhutong): use vmap to boost the training efficiency
+        # self.experts_vmap = torch.vmap(self.experts)
+        self.multiply_gate_scores = multiply_gate_scores
+        self.score_scale_factor = score_scale_factor
+        self.num_experts = experts.num_experts
+        self.mlp_norm = None
+        if multiply_gate_scores and add_weight_norm:
+            raise NotImplementedError
+
+    def reset_experts(self):
+        self.experts.reset_parameters()
+
+    def forward(
+        self, x, topK_indices, topK_scores, expert_batch_size=None, **kwargs
+    ) -> CalculatorOutput:
+        batch_size = topK_indices.size(0)  # topK_indices: (bsz*seq_len, num_selects)
+        num_selects = topK_indices.size(1)
+        topK_indices = topK_indices.flatten()  # shape(batch_size*num_selects)
+        topK_scores = topK_scores.flatten()  # shape(batch_size*num_selects)
+        batch_indices = torch.arange(
+            batch_size, device=topK_scores.device
+        ).repeat_interleave(num_selects)
+
+        _, index_sorted_topK_indices = topK_indices.sort(0)
+
+        sorted_topK_scores = topK_scores.index_select(0, index_sorted_topK_indices)
+        sorted_batch_indices = batch_indices.index_select(0, index_sorted_topK_indices)
+
+        if expert_batch_size is None:
+            expert_batch_size = topK_indices.bincount(
+                minlength=self.num_experts
+            ).tolist()
+
+        sorted_x = x.index_select(0, sorted_batch_indices)
+        split_x = torch.split(sorted_x, expert_batch_size, dim=0)
+
+        expert_outputs = [
+            self.experts(split_x[i], i)
+            for i in range(self.num_experts)
+            if split_x[i].shape[0] > 0
+        ]
+
+        # (bsz*seq_len*num_selects, hidden_size)
+        cat_expert_outputs = torch.cat(expert_outputs, 0)
+        output_dim = cat_expert_outputs.size(1)
+        if self.multiply_gate_scores:
+            if self.mlp_norm is None:
+                cat_expert_outputs = torch.mul(
+                    cat_expert_outputs,
+                    sorted_topK_scores.reshape(-1, 1) * self.score_scale_factor,
+                )
+                # cat_expert_outputs = torch.mul(cat_expert_outputs, sorted_topK_scores.reshape(-1, 1) * 1.0)
+            else:
+                cat_expert_outputs = torch.mul(
+                    cat_expert_outputs, sorted_topK_scores.reshape(-1, 1)
+                )
+                cat_expert_outputs = self.mlp_norm(cat_expert_outputs)
+
+        zeros = torch.zeros(
+            (batch_size, output_dim),
+            device=cat_expert_outputs.device,
+            dtype=cat_expert_outputs.dtype,
+        )
+        y = zeros.index_add(0, sorted_batch_indices, cat_expert_outputs)
+
+        return CalculatorOutput(hidden_states=y, num_dropped_tokens=torch.tensor(-1.0))
+
+
+class BaseMoELayer(nn.Module):
+    def __init__(self):
+        super(BaseMoELayer, self).__init__()
+
+        self.gate: TopKBalancedNoisyGate
+        self.calculator: UniversalCalculator
+
+    def _create_gate(self, **kwargs):
+        self.gate_type = kwargs.get("gate_type", "TopKBalancedNoisyGate")
+
+        if self.gate_type == "TopKBalancedNoisyGate":  # noisy gate
+            self.gate = TopKBalancedNoisyGate(
+                self.input_size,
+                self.num_experts,
+                self.num_selects,
+                gate_network=kwargs.get("gate_network", "mlp"),
+                use_softmax=kwargs.get("gate_use_softmax", True),
+                use_balance=kwargs.get("gate_use_balance", True),
+                balance_loss_weight=kwargs.get("gate_balance_loss_weight", 1e-2),
+                add_noise=kwargs.get("gate_add_noise", True),
+                noise_epsilon=kwargs.get("gate_noise_epsilon", 1e-2),
+            )
+        else:
+            raise NotImplementedError
+
+    def _create_calculator(self, experts, **kwargs):
+        self.calculator_type = kwargs.get("calculator_type", "UniversalCalculator")
+
+        if self.calculator_type == "UniversalCalculator":  # top K calculator
+            self.calculator = UniversalCalculator(
+                experts,
+                multiply_gate_scores=kwargs.get("multiply_gate_scores", True),
+                score_scale_factor=kwargs.get("score_scale_factor", 1.0),
+                add_weight_norm=kwargs.get("add_weight_norm", False),
+            )
+        else:
+            raise NotImplementedError
+
+    def forward(self, x, attention_mask=None) -> MoEMlpOutput:
+        original_shape = x.shape[:-1]
+        x = x.reshape(-1, self.input_size)
+        flattened_mask = None
+        if attention_mask is not None and len(attention_mask.shape) == 2:
+            flattened_mask = attention_mask.flatten()
+            flattened_shape = flattened_mask.shape
+            x = x[flattened_mask.bool()]
+
+        gate_outputs: dict = self.gate(x)
+        calc_outs: CalculatorOutput = self.calculator(x, **gate_outputs)
+
+        y = calc_outs.hidden_states
+        if flattened_mask is not None:
+            y = torch.zeros(flattened_shape + (self.output_size,), dtype=x.dtype, device=x.device)  # (batch_size*seq_len, output_size)
+            y[flattened_mask.bool()] = calc_outs.hidden_states  # (non_padding_num, output_size)
+        y = y.reshape(original_shape + (self.output_size,))
+
+        return MoEMlpOutput(
+            hidden_states=y,
+            balance_loss=gate_outputs.get("balance_loss"),
+            num_dropped_tokens=calc_outs.num_dropped_tokens,
+            gate_load=gate_outputs.get("load", torch.tensor(-1)),
+            gate_importance=gate_outputs.get("importance", torch.tensor(-1)),
+        )
+
+    def reset_gate_network(self):
+        self.gate.reset_gate_network()
+
+    def reset_experts(self):
+        self.calculator.reset_experts()
+
+
+class LinearGLUMoELayer(BaseMoELayer):
+    def __init__(
+        self,
+        input_size,
+        hidden_size,
+        output_size,
+        hidden_act,
+        num_experts,
+        num_selects,
+        size_experts=None,
+        bias=True,
+        **kwargs,
+    ):
+        super(LinearGLUMoELayer, self).__init__()
+        assert num_selects <= num_experts
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.output_size = output_size
+        self.hidden_act = hidden_act
+        self.num_experts = num_experts
+        self.num_selects = num_selects
+        self.size_experts = size_experts
+        self.bias = bias
+
+        experts = LinearGLUExperts(
+            input_size,
+            hidden_size,
+            output_size,
+            hidden_act,
+            num_experts,
+            size_experts=size_experts,
+            bias=bias,
+        )
+
+        self._create_gate(**kwargs)
+        self._create_calculator(experts, **kwargs)
+
+
+class LlamaMoEDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaMoEConfig, layer_index):
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        # self.self_attn = LlamaAttention(config=config)
+        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_index)
+
+        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)
+
+        gating_config = {
+            # all gates
+            "gate_type": config.gate_type,
+            "gate_network": config.gate_network,
+            "gate_use_softmax": config.gate_use_softmax,
+            "gate_use_balance": config.gate_use_balance,
+            "gate_balance_loss_weight": config.gate_balance_loss_weight,
+            "gate_add_noise": config.gate_add_noise,
+            # TopKBalancedNoisyGate
+            "gate_noise_epsilon": config.gate_noise_epsilon,
+        }
+        calculator_config = {
+            # all calculators
+            "calculator_type": config.calculator_type,
+            "multiply_gate_scores": config.multiply_gate_scores,
+            "score_scale_factor": (
+                config.score_scale_factor[layer_index]
+                if isinstance(config.score_scale_factor, list)
+                else config.score_scale_factor
+            ),
+            "add_weight_norm": config.add_weight_norm,
+            # SwitchDropTokenCalculator
+            "drop_tokens": config.drop_tokens,
+            "dropped_padding": config.dropped_padding,
+            "capacity_factor": config.capacity_factor,
+        }
+
+        self.mlp = LinearGLUMoELayer(
+            input_size=self.hidden_size,
+            hidden_size=config.intermediate_size,
+            output_size=self.hidden_size,
+            hidden_act=config.hidden_act,
+            num_experts=config.num_experts,
+            num_selects=config.num_selects,
+            size_experts=(
+                config.size_experts[layer_index]
+                if config.size_experts is not None
+                else None
+            ),
+            bias=False,
+            **gating_config,
+            **calculator_config,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_ids=None,
+        past_key_value=None,
+        output_attentions=False,
+        use_cache=False,
+    ) -> tuple:
+        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)
+        mlp_outs: MoEMlpOutput = self.mlp(hidden_states, attention_mask=attention_mask)
+        hidden_states = residual + mlp_outs.hidden_states
+
+        outputs = (
+            hidden_states,
+            mlp_outs.balance_loss,
+            mlp_outs.num_dropped_tokens,
+            mlp_outs.gate_load,
+            mlp_outs.gate_importance,
+        )
+        if output_attentions:
+            outputs += (self_attn_weights,)
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class LlamaMoEPreTrainedModel(PreTrainedModel):
+    config_class = LlamaMoEConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaMoEDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+
+    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_()
+
+
+class LlamaMoEModel(LlamaMoEPreTrainedModel):
+    def __init__(self, config: LlamaMoEConfig):
+        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(
+            [LlamaMoEDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
+        )
+        self._use_sdpa = config._attn_implementation == "sdpa"
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at"
+                " the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        if 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
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_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.embed_tokens(input_ids)
+
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._use_sdpa and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        hidden_states = inputs_embeds
+        balance_loss = 0.0
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        num_dropped_tokens = ()
+        gate_load = ()
+        gate_importance = ()
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+            if layer_outputs[1] is not None:
+                balance_loss += layer_outputs[1]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[6 if output_attentions else 5]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[5],)
+
+            num_dropped_tokens += (layer_outputs[2],)
+            gate_load += (layer_outputs[3],)
+            gate_importance += (layer_outputs[4],)
+
+        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 = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        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 BaseMoEModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            balance_loss=balance_loss,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            num_dropped_tokens=num_dropped_tokens,
+            gate_load=gate_load,
+            gate_importance=gate_importance,
+        )
+
+    def reset_gate_network(self):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.reset_gate_network()
+
+    def reset_experts(self):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.reset_experts()
+
+
+class LlamaMoEForCausalLM(LlamaMoEPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaMoEModel(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
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        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: BaseMoEModelOutputWithPast = 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.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+            if outputs.balance_loss is not None and outputs.balance_loss > 0:
+                loss += outputs.balance_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return MoECausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            num_dropped_tokens=outputs.num_dropped_tokens,
+            balance_loss=outputs.balance_loss,
+            gate_load=outputs.gate_load,
+            gate_importance=outputs.gate_importance,
+        )
+
+    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:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_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 reset_gate_network(self):
+        self.model.reset_gate_network()
+
+    def reset_experts(self):
+        self.model.reset_experts()

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<unk>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

@@ -0,0 +1,43 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<unk>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}