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configuration_gemmoe.py

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+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+""" Gemmoe model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+GEMMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "Crystalcareai/GemMoE-Beta-1": "https://huggingface.co/Crystalcareai/GemMoE-Beta-1/resolve/main/config.json",
+}
+
+
+class GemmoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GemmoeModel`]. It is used to instantiate a Gemmoe
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the Gemmoe-7B.
+
+    e.g. [mhenrichsen/gemmoe-7b](https://huggingface.co/mhenrichsen/gemmoe-7b)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 256000):
+            Vocabulary size of the Gemmoe model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GemmoeModel`]
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 24576):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 28):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to 
+            `num_attention_heads`.
+        head_dim (`int`, *optional*, defaults to 256):
+            The attention head dimension.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the decoder.  
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-6):  
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        eos_token_id (`int`, *optional*, defaults to 1):
+            End of stream token id.  
+        bos_token_id (`int`, *optional*, defaults to 2):
+            Beginning of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`): 
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts used in the sparse mixture of experts layer.
+        num_local_experts (`int`, *optional*, defaults to 8):  
+            The number of local experts used in the sparse mixture of experts layer.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
+            The coefficient for the auxiliary loss of the router.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not to output the logits of the routers. They are useful for computing the router loss, and
+            should not be returned during inference.
+
+    ```python
+    >>> from transformers import GemmoeModel, GemmoeConfig
+
+    >>> # Initializing a Gemmoe gemmoe-7b style configuration
+    >>> configuration = GemmoeConfig()
+
+    >>> # Initializing a model from the gemmoe-7b style configuration
+    >>> model = GemmoeModel(configuration)
+
+    >>> # Accessing the model configuration 
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "gemmoe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=256000,
+        hidden_size=3072,
+        intermediate_size=24576,
+        num_hidden_layers=28,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        head_dim=256,
+        hidden_act="gelu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        bos_token_id=2,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=8,
+        router_aux_loss_coef=0.01,
+        output_router_logits=False,
+        **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.head_dim = head_dim
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias 
+        self.attention_dropout = attention_dropout
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.output_router_logits = output_router_logits
+         
+        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,
+        )

modeling_gemmoe.py

@@ -0,0 +1,1477 @@
+# coding=utf-8
+# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
+#
+#
+# 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 Gemmoe model."""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...modeling_attn_mask_utils import (
+    _prepare_4d_causal_attention_mask,
+)
+from ...modeling_outputs import SequenceClassifierOutputWithPast, MoeModelOutputWithPast, MoeCausalLMOutputWithPast
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from ...utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from ...utils.import_utils import is_torch_fx_available
+from .configuration_gemmoe import GemmoeConfig
+
+from math import sqrt as math_sqrt
+
+
+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
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "GemmoeConfig"
+
+def approx_gelu(x):
+    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * x**3)))
+
+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,
+    )
+
+
+class GemmoeRMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * (self.weight + 1)
+
+ALL_LAYERNORM_LAYERS.append(GemmoeRMSNorm)
+
+class GemmoeRotaryEmbedding(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
+        self._set_cos_sin_cache(seq_len=max_position_embeddings, device=device, dtype=torch.get_default_dtype())
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        freq_exponents = (2.0 / self.dim) * (
+            torch.arange(self.dim // 2, dtype=torch.int64, device="cpu").float()
+        )
+        timescale = self.base ** freq_exponents
+        positions = torch.arange(self.max_seq_len_cached, device="cpu", dtype=torch.int64).float()
+        radians_new = positions[..., None] / timescale[None, None, :]
+        radians_new = radians_new.squeeze(0)
+        emb = torch.cat((radians_new, radians_new), dim=-1)
+        cos = emb.cos().to(device=device, non_blocking=True)
+        sin = emb.sin().to(device=device, non_blocking=True)
+        self.register_buffer("cos_cached", cos, persistent=False)
+        self.register_buffer("sin_cached", sin, persistent=False)
+
+    def forward(self, x, position_ids=None, seq_len=None):
+        if seq_len is None:
+            seq_len = x.size(2)
+        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],
+            self.sin_cached[:seq_len],
+        )
+
+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=None):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    seq_len, dim = q.shape[-2], q.shape[-1]
+    cos = cos[:seq_len].view(1, 1, seq_len, dim)
+    sin = sin[:seq_len].view(1, 1, seq_len, dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class GemmoeMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+
+        self.act_fn = approx_gelu
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+def load_balancing_loss_func(
+	self,
+	gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
+) -> float:
+	r"""
+	Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+	See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
+	function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+	experts is too unbalanced.
+
+	Args:
+		gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
+			Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+			shape [batch_size X sequence_length, num_experts].
+		attention_mask (`torch.Tensor`, None):
+			The attention_mask used in forward function
+			shape [batch_size X sequence_length] if not None.
+		num_experts (`int`, *optional*):
+			Number of experts
+
+	Returns:
+		The auxiliary loss.
+	"""
+	if gate_logits is None or not isinstance(gate_logits, tuple):
+		return 0
+
+	if isinstance(gate_logits, tuple):
+		compute_device = gate_logits[0].device
+		concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+	routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+	_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+	expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+	if attention_mask is None:
+		# Compute the percentage of tokens routed to each experts
+		tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+		# Compute the average probability of routing to these experts
+		router_prob_per_expert = torch.mean(routing_weights, dim=0)
+	else:
+		batch_size, sequence_length = attention_mask.shape
+		num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+		# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+		expert_attention_mask = (
+			attention_mask[None, :, :, None, None]
+			.expand((num_hidden_layers, batch_size, sequence_length, 2, num_experts))
+			.reshape(-1, 2, num_experts)
+			.to(compute_device)
+		)
+		# Compute the percentage of tokens routed to each experts
+		tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+			expert_attention_mask, dim=0
+		)
+
+		# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+		router_per_expert_attention_mask = (
+			attention_mask[None, :, :, None]
+			.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+			.reshape(-1, num_experts)
+			.to(compute_device)
+		)
+		# Compute the average probability of routing to these experts
+		router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+			router_per_expert_attention_mask, dim=0
+		)
+
+	overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+	return overall_loss * num_experts
+
+def repeat_kv(self, 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 GemmoeAttention(nn.Module):
+    """
+    Multi-headed attention module for Gemmoe model.
+
+    Args:
+        config (GemmoeConfig): The configuration object for the Gemmoe model.
+        layer_idx (Optional[int]): The index of the layer. Default is None.
+    """
+
+    def __init__(self, config: GemmoeConfig, 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 a `layer_idx` is not recommended and will "
+                "lead 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 = 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.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if self.hidden_size % self.num_heads != 0:
+            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=False)
+        self.rotary_emb = GemmoeRotaryEmbedding(
+				self.head_dim,
+				max_position_embeddings=self.max_position_embeddings,
+				base=self.rope_theta,
+			)
+
+    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,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """
+        Forward pass of the attention module.
+
+        Args:
+            hidden_states (torch.Tensor): The input hidden states.
+            attention_mask (Optional[torch.Tensor]): The attention mask. Default is None.
+            position_ids (Optional[torch.LongTensor]): The position IDs. Default is None.
+            past_key_value (Optional[Cache]): The past key-value cache. Default is None.
+            output_attentions (bool): Whether to output the attention weights. Default is False.
+            use_cache (bool): Whether to use caching. Default is False.
+            cache_position (Optional[torch.LongTensor]): The cache position. Default is None.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+                - The output hidden states.
+                - The attention weights (if `output_attentions=True`).
+                - The past key-value cache (if `use_cache=True`).
+        """
+        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)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+            key_states = self.repeat_kv(key_states, self.num_key_value_groups)
+            value_states = self.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 attention_mask is not None:  # no matter the length, we just slice it
+            if cache_position is not None:
+                causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
+            else:
+                causal_mask = attention_mask
+            attn_weights = attn_weights + causal_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.view(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+class GemmoeFlashAttention2(GemmoeAttention):
+    """
+    Gemmoe flash attention module. This module inherits from `GemmoeAttention` 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: Remove this attribute once Flash Attention for RoCm is bumped to 2.1.
+        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,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        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)
+
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            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. (GemmoeRMSNorm 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, -1).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 GemmoeFlashAttention2 __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 GemmoeSdpaAttention(GemmoeAttention):
+    """
+    Gemmoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    GemmoeAttention as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+    def repeat_kv(self, 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)
+    
+    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,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> 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(
+            "GemmoeModel is using GemmoeSdpaAttention, 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,
+                cache_position=cache_position,
+			),
+            
+        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)
+
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = self.repeat_kv(key_states, self.num_key_value_groups)
+        value_states = self.repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None and cache_position is not None:
+            causal_mask = causal_mask[:, :, cache_position, : key_states.shape[-2]]
+            
+            # Cast query, key, and value states to the same dtype (bf16)
+            query_states = query_states.to(dtype=torch.bfloat16)
+            key_states = key_states.to(dtype=torch.bfloat16)
+            value_states = value_states.to(dtype=torch.bfloat16)
+
+        # 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 causal_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=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+GEMMOE_ATTENTION_CLASSES = {
+	"eager": GemmoeAttention,
+	"flash_attention_2": GemmoeFlashAttention2,
+	"sdpa": GemmoeSdpaAttention,
+	}
+
+class GemmoeBlockSparseTop2MLP(nn.Module):
+    def __init__(self, config: GemmoeConfig):
+        super().__init__()
+        self.ffn_dim = config.intermediate_size
+        self.hidden_dim = config.hidden_size
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class GemmoeSparseMoeBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_dim = config.hidden_size
+        self.ffn_dim = config.intermediate_size
+        self.num_experts = config.num_local_experts
+        self.top_k = 2
+
+        # gating
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+        self.experts = nn.ModuleList([GemmoeBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = hidden_states.to(self.gate.weight.device)
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float32)
+        top_routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        top_routing_weights /= top_routing_weights.sum(dim=-1, keepdim=True)
+
+        # we cast back to the input dtype
+        top_routing_weights = top_routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            token_indices = (selected_experts == expert_idx).any(dim=-1).nonzero(as_tuple=True)[0]
+
+            if token_indices.numel() == 0:
+                continue
+
+            current_state = hidden_states[token_indices]
+            current_hidden_states = expert_layer(current_state)
+
+            # Multiply the output hidden states by `top_routing_weights` on the corresponding tokens
+            expert_indices = (selected_experts[token_indices] == expert_idx).nonzero(as_tuple=True)[1]
+            current_hidden_states *= top_routing_weights[token_indices, expert_indices, None]
+
+            final_hidden_states.index_add_(0, token_indices, current_hidden_states)
+
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+    
+class GemmoeDecoderLayer(nn.Module):
+    """
+    Decoder layer for the Gemmoe model.
+
+    Args:
+        config (GemmoeConfig): The configuration object for the Gemmoe model.
+        layer_idx (int): The index of the layer.
+    """
+    def __init__(self, config: GemmoeConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = GEMMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+        self.mlp = GemmoeMLP(config)
+        self.block_sparse_moe = GemmoeSparseMoeBlock(config)
+        self.input_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) 
+        self.post_attention_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            output_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+                should not be returned during inference.
+        """
+        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.`"
+            )
+
+        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,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states, router_logits = self.block_sparse_moe(hidden_states)
+        hidden_states = residual + hidden_states
+    
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+GEMMOE_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.)
+"""
+
+@add_start_docstrings(
+"The bare Gemmoe Model outputting raw hidden-states without any specific head on top.",
+GEMMOE_START_DOCSTRING,
+)
+
+class GemmoePreTrainedModel(PreTrainedModel):
+	config_class = GemmoeConfig
+	base_model_prefix = "model"
+	supports_gradient_checkpointing = True
+	_keep_in_fp32_modules = ["inv_freq", "rotary_emb", "cos_cached", "sin_cached"]
+	_no_split_modules = ["GemmoeDecoderLayer"]
+	_skip_keys_device_placement = ["past_key_values", "causal_mask"]
+	_supports_flash_attn_2 = True
+	_supports_sdpa = True
+	_supports_cache_class = 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_()
+
+	def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
+		if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
+			raise ValueError(
+				"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+				"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+			)
+		if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device:
+			causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=1, device=self.device)
+			self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
+
+		for layer in self.model.layers:
+			weights = layer.self_attn.o_proj.weight
+			layer.self_attn.past_key_value = cache_cls(
+				self.config, max_batch_size, max_cache_len, device=weights.device, dtype=weights.dtype
+			)
+
+	def _reset_cache(self):
+		for layer in self.model.layers:
+			layer.self_attn.past_key_value = None
+
+GEMMOE_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.
+"""
+
+@add_start_docstrings(
+"The bare Gemmoe Model outputting raw hidden-states without any specific head on top.",
+GEMMOE_START_DOCSTRING,
+)
+
+class GemmoeModel(GemmoePreTrainedModel):
+	"""
+	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [GemmoeDecoderLayer]Args:
+		config: GemmoeConfig
+	"""
+
+
+	def __init__(self, config: GemmoeConfig):
+		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(
+			[GemmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+		)
+		self.norm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+		self.gradient_checkpointing = False
+
+		# Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
+		# NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_position_embeddings`.
+		causal_mask = torch.full(
+			(config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool
+		)
+		self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=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
+
+	@add_start_docstrings_to_model_forward(GEMMOE_INPUTS_DOCSTRING)
+	@replace_return_docstrings(output_type=MoeModelOutputWithPast, 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,
+		use_cache: Optional[bool] = None,
+		output_attentions: Optional[bool] = None,
+		output_hidden_states: Optional[bool] = None,
+		output_router_logits: Optional[bool] = None,
+		return_dict: Optional[bool] = None,
+		cache_position: Optional[torch.LongTensor] = None,
+	) -> Union[Tuple, MoeModelOutputWithPast]:
+		"""
+		Forward pass of the sequence classification model.
+
+		Args:
+			input_ids: Input token IDs.
+			attention_mask: Attention mask.
+			position_ids: Position IDs.
+			past_key_values: Past key-value pairs.
+			inputs_embeds: Input embeddings.
+			labels: Labels for sequence classification.
+			use_cache: Whether to use cache.
+			output_attentions: Whether to output attentions.
+			output_hidden_states: Whether to output hidden states.
+			return_dict: Whether to return a dictionary or tuple.
+
+		Returns:
+			Output of the sequence classification model.
+		"""
+		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
+		)
+		output_router_logits = (
+			output_router_logits if output_router_logits is not None else self.config.output_router_logits
+		)
+		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
+
+		if (input_ids is None) ^ (inputs_embeds is not None):
+			raise ValueError(
+				"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+			)
+
+		if self.gradient_checkpointing and self.training and use_cache:
+			logger.warning_once(
+				"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+			)
+			use_cache = False
+
+		if inputs_embeds is None:
+			inputs_embeds = self.embed_tokens(input_ids)
+
+		past_seen_tokens = 0
+		if use_cache:  # kept for BC (cache positions)
+			if not isinstance(past_key_values, StaticCache):
+				past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+			past_seen_tokens = past_key_values.get_seq_length()
+
+		if cache_position is None:
+			cache_position = torch.arange(
+				past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+			)
+
+		if position_ids is None:
+			position_ids = cache_position.unsqueeze(0)
+
+		causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
+
+		hidden_states = inputs_embeds
+
+		# Normalize
+		scale_factor = torch.tensor(math_sqrt(self.config.hidden_size), dtype=hidden_states.dtype)
+		hidden_states = hidden_states * scale_factor
+		# Decoder layers
+		all_hidden_states = () if output_hidden_states else None
+		all_self_attns = () if output_attentions else None
+		all_router_logits = () if output_router_logits else None
+		next_decoder_cache = None
+
+		for decoder_layer in 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,
+					causal_mask,
+					position_ids,
+					past_key_values,
+					output_attentions,
+					output_router_logits,
+					use_cache,
+					cache_position,
+				)
+			else:
+				layer_outputs = decoder_layer(
+					hidden_states,
+					attention_mask=causal_mask,
+					position_ids=position_ids,
+					past_key_value=past_key_values,
+					output_attentions=output_attentions,
+					output_router_logits=output_router_logits,
+					use_cache=use_cache,
+					cache_position=cache_position,
+				)
+
+			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],)
+			if output_router_logits:
+				all_router_logits += (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 = None
+		if use_cache:
+			next_cache = (
+				next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, 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, all_router_logits] if v is not None)
+
+		return MoeModelOutputWithPast(
+			last_hidden_state=hidden_states,
+			past_key_values=next_cache,
+			hidden_states=all_hidden_states,
+			attentions=all_self_attns,
+			router_logits=all_router_logits
+		)
+
+	def _update_causal_mask(self, attention_mask, input_tensor):
+		"""
+		Update the causal mask based on the attention mask and input tensor.
+
+		Args:
+			attention_mask (torch.Tensor): The attention mask.
+			input_tensor (torch.Tensor): The input tensor.
+
+		Returns:
+			torch.Tensor: The updated causal mask.
+		"""
+
+		if self.config._attn_implementation == "flash_attention_2":
+			if attention_mask is not None and 0.0 in attention_mask:
+				return attention_mask
+			return None
+
+		batch_size, seq_length = input_tensor.shape[:2]
+		dtype = input_tensor.dtype
+		device = input_tensor.device
+
+		# support going beyond cached `max_position_embedding`
+		if seq_length > self.causal_mask.shape[-1]:
+			logger.info(f"Resizing causal mask buffer from {self.causal_mask.shape[-1]} to {2 * self.causal_mask.shape[-1]}")
+			causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
+			self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
+
+		# We use the current dtype to avoid any overflows
+		min_dtype = torch.finfo(dtype).min
+		causal_mask = self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * min_dtype
+		causal_mask = causal_mask.to(dtype=dtype, device=device)
+		
+		if attention_mask is not None and attention_mask.dim() == 2:
+			mask_length = attention_mask.shape[-1]
+			padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
+			causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+		
+		if self.config._attn_implementation == "sdpa" and attention_mask is not None:
+			# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
+			is_tracing = (
+				torch.jit.is_tracing()
+				or isinstance(input_tensor, torch.fx.Proxy)
+				or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
+			)
+			
+			if not is_tracing and torch.any(attention_mask != 1):
+				# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
+				# using left padding. This is required by
+				# F.scaled_dot_product_attention memory-efficient attention path.
+				# Details: https://github.com/pytorch/pytorch/issues/110213
+				causal_mask = causal_mask.mul(~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)).to(dtype)
+
+		return causal_mask
+
+class GemmoeForCausalLM(GemmoePreTrainedModel):
+    r"""
+    The Gemmoe Model transformer with a language modeling head on top for causal language modeling (CLM).
+
+    Args:
+        config (GemmoeConfig): The configuration object for the Gemmoe model.
+
+    Example usage:
+    ```python
+    >>> from transformers import AutoTokenizer, GemmoeForCausalLM
+
+    >>> model = GemmoeForCausalLM.from_pretrained("google/gemmoe-7b")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemmoe-7b")
+
+    >>> prompt = "What is your favorite condiment?"
+    >>> 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]
+    "What is your favorite condiment?"
+    ```
+    """
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = GemmoeModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = 8
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        # 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(GEMMOE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, 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,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        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, GemmoeForCausalLM
+
+        >>> model = GemmoeForCausalLM.from_pretrained("google/gemmoe-7b")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemmoe-7b")
+
+        >>> prompt = "What is your favorite condiment?"
+        >>> 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]
+        "What is your favorite condiment?"
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else getattr(self.config, "output_router_logits", False)
+        )
+        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
+
+        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,
+            output_router_logits=output_router_logits,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+        
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        aux_loss = None
+        if output_router_logits:
+            router_logits = outputs.router_logits if return_dict else outputs[-1]
+            if router_logits is not None:
+                aux_loss = load_balancing_loss_func(
+                    router_logits,
+                    self.num_experts,
+                    self.num_experts_per_tok,
+                    attention_mask,
+                )
+                if labels is not None:
+                    loss += self.router_aux_loss_coef * aux_loss.to(loss.device)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            if aux_loss is not None:
+                output = (aux_loss,) + output
+            return (loss,) + output if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        past_length = 0
+        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
+
+            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) :]
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            
+            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:
+            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 self.generation_config.cache_implementation == "static":
+            cache_position = kwargs.get("cache_position", None)
+            if cache_position is None:
+                past_length = 0
+            else:
+                past_length = cache_position[-1] + 1
+            input_ids = input_ids[:, past_length:]
+            position_ids = position_ids[:, past_length:]
+
+        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
+
+        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.contiguous()}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids.contiguous(),
+                "cache_position": cache_position,
+                "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
+@add_start_docstrings(
+    """
+    The Gemmoe Model transformer with a sequence classification head on top (linear layer).
+    [`GemmoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    GEMMOE_START_DOCSTRING,
+)
+
+class GemmoeForSequenceClassification(GemmoePreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = GemmoeModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(GEMMOE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        """
+        Forward pass of the sequence classification model.
+
+        Args:
+            input_ids (torch.LongTensor, optional): Input token IDs.
+            attention_mask (torch.Tensor, optional): Attention mask.
+            position_ids (torch.LongTensor, optional): Position IDs.
+            past_key_values (List[torch.FloatTensor], optional): Past key-value pairs.
+            inputs_embeds (torch.FloatTensor, optional): Input embeddings.
+            labels (torch.LongTensor, optional): Labels for sequence classification.
+            use_cache (bool, optional): Whether to use cache.
+            output_attentions (bool, optional): Whether to output attentions.
+            output_hidden_states (bool, optional): Whether to output hidden states.
+            return_dict (bool, optional): Whether to return a dictionary or tuple.
+
+        Returns:
+            Union[Tuple, SequenceClassifierOutputWithPast]: Output of the sequence classification model.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

tokenization_gemmoe.py

@@ -0,0 +1,311 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+"""Tokenization classes for Gemmoe."""
+import os
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+
+if TYPE_CHECKING:
+    pass
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+SPIECE_UNDERLINE = "▁"
+
+class GemmoeTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Gemmoe tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
+    no padding token in the original model.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`):
+            The end of sequence token.
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`):
+            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+            attention mechanisms or loss computation.
+        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+                - `nbest_size = {0,1}`: No sampling is performed.
+                - `nbest_size > 1`: samples from the nbest_size results.
+                - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                  using forward-filtering-and-backward-sampling algorithm.
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+        add_bos_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of sequences.
+        add_eos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of sequences.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not the default system prompt for Gemmoe should be used.
+        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
+            Whether or not to add spaces between special tokens.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<bos>",
+        eos_token="<eos>",
+        pad_token="<pad>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        use_default_system_prompt=False,
+        spaces_between_special_tokens=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
+
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.use_default_system_prompt = use_default_system_prompt
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            use_default_system_prompt=use_default_system_prompt,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            **kwargs,
+        )
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text, **kwargs):
+        """
+        Returns a tokenized string. The Gemmoe tokenizer never adds a prefix space.
+        """
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _decode(
+        self,
+        token_ids: List[int],
+        skip_special_tokens: bool = False,
+        spaces_between_special_tokens: bool = False,
+        **kwargs,
+    ) -> str:
+        sub_texts = []
+        current_sub_text = []
+        for ids in token_ids:
+            if skip_special_tokens and ids in self.all_special_ids:
+                continue
+            if ids in self._added_tokens_decoder:
+                if current_sub_text:
+                    sub_texts.append(self.sp_model.decode(current_sub_text))
+                sub_texts.append(self._added_tokens_decoder[ids].content)
+                current_sub_text = []
+            else:
+                current_sub_text.append(ids)
+        if current_sub_text:
+            sub_texts.append(self.sp_model.decode(current_sub_text))
+        if spaces_between_special_tokens:
+            sub_texts = " ".join(sub_texts)
+        else:
+            sub_texts = "".join(sub_texts)
+        return sub_texts
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self._added_tokens_encoder:
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+        output = bos_token_id + token_ids_0 + eos_token_id
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+"""
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+        if token_ids_1 is not None:
+            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+            return output
+
+    def _build_conversation_input_ids(self, conversation: List[List[int]]) -> List[int]:
+        input_ids = []
+        for i, history in enumerate(conversation):
+            if i % 2 == 0:
+                input_ids.extend([self.bos_token_id, self.convert_tokens_to_ids("<start_of_turn>")] + history + [self.convert_tokens_to_ids("<end_of_turn>")])
+            else:
+                input_ids.extend([self.bos_token_id, self.convert_tokens_to_ids("<start_of_turn>"), self.convert_tokens_to_ids("model")] + history + [self.convert_tokens_to_ids("<end_of_turn>\n")])
+        input_ids.append(self.eos_token_id)
+        return input_ids