Update modeling_gemmoe.py

modeling_gemmoe.py

@@ -26,11 +26,14 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
+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.modeling_outputs import SequenceClassifierOutputWithPast, MoeModelOutputWithPast, MoeCausalLMOutputWithPast
+from transformers.modeling_outputs import SequenceClassifierOutputWithPast, BaseModelOutputWithPast, CausalLMOutputWithPast
 from transformers.modeling_utils import PreTrainedModel
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
 from transformers.utils import (
@@ -60,7 +63,6 @@ if is_torch_fx_available():
 
     _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
 
-
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "GemmoeConfig"
@@ -156,55 +158,121 @@ def _get_unpad_data(attention_mask):
         max_seqlen_in_batch,
     )
 
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    warnings.warn(
+        "Calling `transformers.models.Gemmoe.modeling_Gemmoe._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+    )
+    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    warnings.warn(
+        "Calling `transformers.models.Gemmoe.modeling_Gemmoe._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.Gemmoe.modeling_Gemmoe.AttentionMaskConverter._make_causal_mask"
+    )
+    return AttentionMaskConverter._make_causal_mask(
+        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+    )
+
 
 
 class GemmoeRMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-6):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        GemmoeRMSNorm is equivalent to T5LayerNorm
+        """
         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)
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
 
-    def forward(self, x):
-        output = self._norm(x.float()).type_as(x)
-        return output * (self.weight + 1)
+    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)
 
 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())
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+        self.max_seq_len_cached = None
+
 
     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:
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq.to(t.device))
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if self.max_seq_len_cached is None or 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],
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
         )
+    
+class GemmoeLinearScalingRotaryEmbedding(GemmoeRotaryEmbedding):
+    """GemmoeRotaryEmbedding 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.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+    
+class GemmoeDynamicNTKScalingRotaryEmbedding(GemmoeRotaryEmbedding):
+    """GemmoeRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
@@ -212,16 +280,199 @@ def rotate_half(x):
     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)
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
     q_embed = (q * cos) + (rotate_half(q) * sin)
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
 
-def repeat_kv(self, hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+class GemmoeMLP(nn.Module):
+    def __init__(self, config, hidden_size = None, intermediate_size = None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+            )
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+    
+class MoEGate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        # topk selection algorithm
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.hidden_size
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init  as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+    
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape        
+        ### compute gating score
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+        
+        ### select top-k experts
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+        
+        ### norm gate to sum 1
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        ### expert-level computation auxiliary loss
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            # always compute aux loss based on the naive greedy topk method
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss, torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim = 1)).sum(dim = 1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = None
+        return topk_idx, topk_weight, aux_loss
+    
+class AddAuxiliaryLoss(torch.autograd.Function):
+    """
+    The trick function of adding auxiliary (aux) loss, 
+    which includes the gradient of the aux loss during backpropagation.
+    """
+    @staticmethod
+    def forward(ctx, x, loss):
+        assert loss.numel() == 1
+        ctx.dtype = loss.dtype
+        ctx.required_aux_loss = loss.requires_grad
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_loss = None
+        if ctx.required_aux_loss:
+            grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
+        return grad_output, grad_loss
+    
+class GemMoE(nn.Module):
+    """
+    A mixed expert module containing shared experts.
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.experts = nn.ModuleList([GemmoeMLP(config, intermediate_size = config.moe_intermediate_size) for i in range(config.n_routed_experts)])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = GemmoeMLP(config=config, intermediate_size = intermediate_size)
+    
+    def forward(self, hidden_states):
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
+            y = torch.empty_like(hidden_states)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y =  y.view(*orig_shape)
+            y = AddAuxiliaryLoss.apply(y, aux_loss)
+        else:
+            y = self.moe_infer(hidden_states, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        return y
+    
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.num_experts_per_tok
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i-1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum')
+        return expert_cache
+    
+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)
@@ -231,15 +482,10 @@ def repeat_kv(self, hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
         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.
-    """
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
 
     def __init__(self, config: GemmoeConfig, layer_idx: Optional[int] = None):
         super().__init__()
@@ -247,34 +493,62 @@ class GemmoeAttention(nn.Module):
         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` "
+                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 = config.head_dim
+        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.hidden_size % self.num_heads != 0:
+        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=False)
-        self.rotary_emb = GemmoeRotaryEmbedding(
-				self.head_dim,
-				max_position_embeddings=self.max_position_embeddings,
-				base=self.rope_theta,
-			)
+        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 = GemmoeRotaryEmbedding(
+                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 = GemmoeLinearScalingRotaryEmbedding(
+                    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 = GemmoeDynamicNTKScalingRotaryEmbedding(
+                    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,
@@ -284,64 +558,78 @@ class GemmoeAttention(nn.Module):
         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.
+        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.`"
+            )
 
-        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)
+        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)
 
-        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)
+        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:
-            # 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}
+            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 = self.repeat_kv(key_states, self.num_key_value_groups)
-            value_states = self.repeat_kv(value_states, self.num_key_value_groups)
+        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 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
+        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):
@@ -351,9 +639,15 @@ class GemmoeAttention(nn.Module):
             )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, -1)
 
-        attn_output = self.o_proj(attn_output)
+        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
@@ -366,9 +660,13 @@ class GemmoeFlashAttention2(GemmoeAttention):
     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.
+
+        # 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(
@@ -379,9 +677,17 @@ class GemmoeFlashAttention2(GemmoeAttention):
         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]]]:
+        # GemmoeFlashAttention2 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()
@@ -397,13 +703,14 @@ class GemmoeFlashAttention2(GemmoeAttention):
         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)
+        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)
 
-        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}
+            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
@@ -419,13 +726,14 @@ class GemmoeFlashAttention2(GemmoeAttention):
         # 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"):
+            if hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             else:
                 target_dtype = self.q_proj.weight.dtype
 
@@ -434,6 +742,7 @@ class GemmoeFlashAttention2(GemmoeAttention):
                 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)
@@ -442,7 +751,7 @@ class GemmoeFlashAttention2(GemmoeAttention):
             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 = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -484,6 +793,7 @@ class GemmoeFlashAttention2(GemmoeAttention):
             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
 
@@ -499,6 +809,7 @@ class GemmoeFlashAttention2(GemmoeAttention):
                 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(
@@ -509,15 +820,14 @@ class GemmoeFlashAttention2(GemmoeAttention):
 
     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
@@ -549,21 +859,11 @@ class GemmoeFlashAttention2(GemmoeAttention):
 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
+    `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, x, n_rep):
-        """
-        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 = x.shape
-        if n_rep == 1:
-            return x
-        x = x[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-        return x.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
+    # Adapted from GemmoeAttention.forward
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -572,15 +872,13 @@ class GemmoeSdpaAttention(GemmoeAttention):
         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.'
-            # )
-            
+            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,
@@ -588,9 +886,8 @@ class GemmoeSdpaAttention(GemmoeAttention):
                 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)
@@ -601,48 +898,46 @@ class GemmoeSdpaAttention(GemmoeAttention):
         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)
+        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)
 
-        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}
+            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 = self.repeat_kv(key_states, self.num_key_value_groups)
-        value_states = self.repeat_kv(value_states, self.num_key_value_groups)
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = 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]]
-
-        # Ensure query, key, and value states have the same dtype
-        common_dtype = query_states.dtype
-        key_states = key_states.to(dtype=common_dtype)
-        value_states = value_states.to(dtype=common_dtype)
+        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 causal_mask is not None:
+        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()
 
-        # Cast causal_mask to the same dtype as query_states
-        if causal_mask is not None:
-            causal_mask = causal_mask.to(dtype=query_states.dtype)
-        
         attn_output = torch.nn.functional.scaled_dot_product_attention(
             query_states,
             key_states,
             value_states,
-            attn_mask=causal_mask,
+            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.view(bsz, q_len, -1)
+        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
@@ -653,74 +948,17 @@ GEMMOE_ATTENTION_CLASSES = {
 	"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 = approx_gelu
-
-    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) -> Tuple[torch.Tensor, torch.Tensor]:
-        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.float)
-        topk_weight, topk_idx = torch.topk(routing_weights, self.top_k, dim=-1, sorted=False)
-        topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
-
-        # we cast back to the input dtype
-        topk_weight = topk_weight.to(hidden_states.dtype)
 
-        hidden_states = hidden_states.repeat_interleave(self.top_k, dim=0)
-
-        y = torch.empty_like(hidden_states)
-
-        flat_topk_idx = topk_idx.view(-1)
-        for i in range(self.num_experts):
-            expert = self.experts[i]
-            expert_output = expert(hidden_states[flat_topk_idx == i])
-            y[flat_topk_idx == i] = expert_output.to(y.dtype)  # Cast expert_output to the same dtype as y
-
-        y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
-
-        final_hidden_states = y.reshape(batch_size, sequence_length, hidden_dim)
-        return final_hidden_states, router_logits
-
-    
 class GemmoeDecoderLayer(nn.Module):
     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, layer_idx)
+        self.self_attn = GEMMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
 
-        self.block_sparse_moe = GemmoeSparseMoeBlock(config)
+        self.mlp = GemMoE(config) if (config.n_routed_experts is not None and  \
+                                           layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0) \
+                                        else GemmoeMLP(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)
 
@@ -731,9 +969,7 @@ class GemmoeDecoderLayer(nn.Module):
         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]]]:
         """
@@ -749,16 +985,13 @@ class GemmoeDecoderLayer(nn.Module):
                 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
@@ -769,7 +1002,6 @@ class GemmoeDecoderLayer(nn.Module):
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
-            cache_position=cache_position,
             **kwargs,
         )
         hidden_states = residual + hidden_states
@@ -777,9 +1009,8 @@ class GemmoeDecoderLayer(nn.Module):
         # 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 = self.mlp(hidden_states)
         hidden_states = residual + hidden_states
-    
 
         outputs = (hidden_states,)
 
@@ -789,15 +1020,23 @@ class GemmoeDecoderLayer(nn.Module):
         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.)
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`GemmoeConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
 """
 
 @add_start_docstrings(
@@ -806,52 +1045,94 @@ 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.
+    config_class = GemmoeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["GemmoeDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _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_()
+
+
+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.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 @add_start_docstrings(
@@ -860,263 +1141,168 @@ 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).
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmoeDecoderLayer`]
 
     Args:
-        config (GemmoeConfig): The configuration object for the Gemmoe model.
+        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._use_sdpa = config._attn_implementation == "sdpa"
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
-    Example usage:
-    ```python
-    >>> from transformers import AutoTokenizer, GemmoeForCausalLM
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
 
-    >>> model = GemmoeForCausalLM.from_pretrained("google/gemmoe-7b")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemmoe-7b")
+    def get_input_embeddings(self):
+        return self.embed_tokens
 
-    >>> prompt = "What is your favorite condiment?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
 
-    >>> # 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?"
-    ```
-    """
+    @add_start_docstrings_to_model_forward(Gemmoe_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        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`transformers."
+                )
+                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
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions 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,
+                    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 use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = 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 BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+class GemmoeForCausalLM(GemmoePreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
@@ -1124,9 +1310,6 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
         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()
@@ -1149,8 +1332,8 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
     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)
+    @add_start_docstrings_to_model_forward(Gemmoe_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1162,16 +1345,14 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
         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]:
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
         r"""
         Args:
             labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
                 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]`.
+                (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
 
         Returns:
 
@@ -1180,26 +1361,24 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
         ```python
         >>> from transformers import AutoTokenizer, GemmoeForCausalLM
 
-        >>> model = GemmoeForCausalLM.from_pretrained("google/gemmoe-7b")
-        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemmoe-7b")
+        >>> model = GemmoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
 
-        >>> prompt = "What is your favorite condiment?"
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
 
         >>> # Generate
         >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "What is your favorite condiment?"
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_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
 
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
         outputs = self.model(
             input_ids=input_ids,
             attention_mask=attention_mask,
@@ -1209,61 +1388,46 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
             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]
-
-        # Ensure hidden_states and lm_head have compatible dtypes
-        hidden_states = hidden_states.to(dtype=self.lm_head.weight.dtype)
-
-        logits = self.lm_head(hidden_states)
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
 
         loss = None
         if labels is not None:
+            # Shift so that tokens < n predict n
             shift_logits = logits[..., :-1, :].contiguous()
             shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
             loss_fct = CrossEntropyLoss()
             shift_logits = shift_logits.view(-1, self.config.vocab_size)
             shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
             shift_labels = shift_labels.to(shift_logits.device)
             loss = loss_fct(shift_logits, shift_labels)
 
-        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(
+        return CausalLMOutputWithPast(
             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()
@@ -1273,11 +1437,19 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
                 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
@@ -1287,37 +1459,26 @@ class GemmoeForCausalLM(GemmoePreTrainedModel):
 
         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 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[:, -1].unsqueeze(-1)
-            position_ids = position_ids[:, -1].unsqueeze(-1)
-
-        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
-
+        # 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.contiguous()}
+            model_inputs = {"input_ids": input_ids}
 
         model_inputs.update(
             {
-                "position_ids": position_ids.contiguous(),
-                "cache_position": cache_position,
+                "position_ids": position_ids,
                 "past_key_values": past_key_values,
                 "use_cache": kwargs.get("use_cache"),
                 "attention_mask": attention_mask,
             }
         )
-
         return model_inputs
 
     @staticmethod
@@ -1350,6 +1511,7 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
         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()
 
@@ -1359,8 +1521,7 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
     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)
+    @add_start_docstrings_to_model_forward(Gemmoe_INPUTS_DOCSTRING)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1374,25 +1535,14 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
         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.
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
         transformer_outputs = self.model(
             input_ids,
             attention_mask=attention_mask,
@@ -1418,8 +1568,9 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
             sequence_lengths = -1
         else:
             if input_ids is not None:
-                sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
-                sequence_lengths = sequence_lengths.clamp(min=0).to(logits.device)
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
             else:
                 sequence_lengths = -1
 
@@ -1448,7 +1599,6 @@ class GemmoeForSequenceClassification(GemmoePreTrainedModel):
             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