Upload ModernALBERTForMaskedLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
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+## Uses
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+
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+
+## How to Get Started with the Model
+
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+## Training Details
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+
+#### Training Hyperparameters
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+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
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+
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config.json

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+{
+  "adapter_reduction": 16,
+  "architectures": [
+    "ModernALBERTForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_modernalbert.ModernALBERTConfig",
+    "AutoModelForMaskedLM": "modeling_modernalbert.ModernALBERTForMaskedLM"
+  },
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "embedding_size": 128,
+  "expert_intermediate_size": 2624,
+  "group_depth": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-06,
+  "load_balancing_loss_coef": 0.1,
+  "lora_alpha": 32,
+  "lora_rank": 16,
+  "max_position_embeddings": 8192,
+  "model_type": "ModernALBERT",
+  "num_attention_heads": 12,
+  "num_expert_modules": 2,
+  "num_experts": 4,
+  "num_hidden_layers": 14,
+  "pad_token_id": 0,
+  "router_jitter_noise": 0.01,
+  "routing_strategy": "standard",
+  "top_k": 1,
+  "transformers_version": "4.56.0",
+  "use_adapter": true,
+  "use_cache": true,
+  "use_moa": true,
+  "vocab_size": 50368
+}

configuration_modernalbert.py

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+from datasets import load_dataset                                      
+from transformers import (
+    AutoTokenizer,
+    AutoModelForMaskedLM,
+    DataCollatorForLanguageModeling,                                   
+    Trainer,
+    TrainingArguments,
+)
+
+from itertools import chain
+import torch
+
+import transformers as ts
+from optimi import StableAdamW                                                    
+import os
+
+from transformers.modeling_outputs import *
+import torch.nn as nn
+import torch
+from dataclasses import dataclass
+from typing import Optional, Tuple
+import transformers as ts
+import gc
+
+from transformers import PretrainedConfig
+import torch.nn.functional as F
+
+from typing import Optional, Union
+
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.activations import ACT2FN
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+import math
+
+class ModernALBERTConfig(PretrainedConfig):
+    """
+    Configuration class for the ModernALBERT model with a LoRA-based MoE.
+    """
+    model_type = "ModernALBERT"
+
+    def __init__(
+        self,
+        # Block Adapter settings
+        use_adapter: bool = True,
+        adapter_reduction: int = 16,
+
+        # LoRA Mixture of Experts (MoE) settings
+        use_moa: bool = True,
+        num_experts: int = 8,
+        expert_intermediate_size = 2048,
+        top_k: int = 2,
+        lora_rank: int = 16,
+        lora_alpha: int = 32,
+        router_jitter_noise: float = 0.01,
+        load_balancing_loss_coef: float = 0.01,
+        num_expert_modules = 3,
+
+        # Layer sharing
+        group_depth: int = 4,
+        
+        # Model dims
+        vocab_size: int = 50368,
+        hidden_size: int = 1024,
+        embedding_size: int = 128,
+        num_hidden_layers: int = 16,
+        num_attention_heads: int = 16,
+        intermediate_size: int = 2624, # Typically 4 * hidden_size
+        
+        # Activation and dropout
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        
+        # Position embeddings
+        max_position_embeddings: int = 8192,
+        
+        # Other standard params
+        pad_token_id: int = 0,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        
+        # Sequence classification
+        num_labels: int = 2,
+        classifier_dropout: float = None,
+        routing_strategy: str = "standard",
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+        
+        self.use_adapter = use_adapter
+        self.adapter_reduction = adapter_reduction
+        self.use_moa = use_moa
+        self.num_experts = num_experts
+        self.top_k = top_k
+        self.lora_rank = lora_rank
+        self.router_jitter_noise = router_jitter_noise
+        self.load_balancing_loss_coef = load_balancing_loss_coef
+        self.group_depth = group_depth
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.embedding_size = embedding_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.expert_intermediate_size = expert_intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.num_labels = num_labels
+        self.classifier_dropout = classifier_dropout
+        self.lora_alpha = lora_alpha
+        self.num_expert_modules = num_expert_modules
+        self.routing_strategy = routing_strategy

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07e724fef0ad384d28b2cfd16fd7697a8340c5901be48a42a07335e3d38a8a2f
+size 205288808

modeling_modernalbert.py

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+from datasets import load_dataset                                      
+from transformers import (
+    AutoTokenizer,
+    AutoModelForMaskedLM,
+    DataCollatorForLanguageModeling,                                   
+    Trainer,
+    TrainingArguments,
+)
+
+from itertools import chain
+import torch
+
+import transformers as ts
+from optimi import StableAdamW                                                    
+import os
+
+from transformers.modeling_outputs import *
+import torch.nn as nn
+import torch
+from dataclasses import dataclass
+from typing import Optional, Tuple
+import transformers as ts
+import gc
+
+from transformers import PretrainedConfig
+import torch.nn.functional as F
+
+from typing import Optional, Union
+
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.activations import ACT2FN
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+import math
+
+# Optional FlashAttention import
+try:
+    from flash_attn.bert_padding import unpad_input, pad_input
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+    from flash_attn.layers.rotary import RotaryEmbedding
+    from flash_attn.ops.triton.rotary import apply_rotary
+    FLASH_ATTN_AVAILABLE = True
+    print("✅ FlashAttention is available.")
+except ImportError:
+    FLASH_ATTN_AVAILABLE = False
+    print("❌ FlashAttention is not available. Using PyTorch SDPA fallback.")
+
+from .configuration_modernalbert import ModernALBERTConfig
+
+# --- Shared FFN (Unchanged) ---
+class SharedLoraFFN(nn.Module):
+    """
+    A shared Feed-Forward Network modified by LoRA weights.
+    The forward pass accepts pre-merged LoRA weights.
+    """
+    def __init__(self, config):
+        super().__init__()
+        dim = config.hidden_size
+        intermediate_dim = config.expert_intermediate_size
+        
+        self.linear1 = nn.Linear(dim, intermediate_dim)
+        self.act = nn.GELU()
+        self.linear2 = nn.Linear(intermediate_dim, dim)
+        self.lora_scaling = config.lora_alpha / config.lora_rank
+
+    def forward(self, x, lora_A1, lora_B1, lora_A2, lora_B2):
+        # Apply the merged LoRA weights
+        # Formula: (x @ A.T @ B.T) * scale
+        expanded = self.linear1(x) + (x @ lora_A1.T @ lora_B1.T) * self.lora_scaling
+        activated = self.act(expanded)
+        contracted = self.linear2(activated) + (activated @ lora_A2.T @ lora_B2.T) * self.lora_scaling
+        return contracted
+
+# --- 1. The Router (Fixed for Flash Attention / Unpadded Inputs) ---
+class SwitchRouterTopK(nn.Module):
+    """
+    Calculates the EMA weights for expert merging.
+    Optimized for unpadded (Flash Attention) inputs where shape is (total_nnz, dim).
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        
+        self.num_experts = config.num_experts
+        # Use a slightly lower decay during training if starting from scratch (e.g., 0.9)
+        self.ema_decay = getattr(config, "router_ema_decay", 0.99) 
+        
+        self.layer = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+        self.k = config.top_k
+        self.jitter_noise = config.router_jitter_noise
+        
+        # Buffer for inference (frozen stats)
+        self.register_buffer("ema_weights", torch.ones(config.num_experts) / config.num_experts)
+        
+    def forward(self, hidden_states):
+        # hidden_states shape: (total_nnz, hidden_size)
+        if self.config.routing_strategy == "ema":
+            # 1. Compute Router Probabilities
+            logits = self.layer(hidden_states) # Shape: (total_nnz, num_experts)
+            probs = F.softmax(logits, dim=-1)
+            
+            if self.training:
+                # 2. Compute batch-level routing vector r_b
+                # Since inputs are unpadded (Batch * Seq flattened to dim 0), 
+                # we simply average across all tokens to get the global batch stats.
+                r_b = probs.mean(dim=0) # Shape: (num_experts,)
+    
+                # 3. Calculate the weight to USE for this step (Allow Gradients!)
+                # We mix history (detached) with current (with grad) to stabilize training.
+                weights_for_forward = self.ema_decay * self.ema_weights.detach() + (1 - self.ema_decay) * r_b
+                
+                # 4. Update the buffer in the background (No Gradients needed for storage)
+                new_ema_value = weights_for_forward.detach()
+                self.ema_weights.copy_(new_ema_value)
+                
+                # Normalize to ensure sum is 1
+                self.ema_weights.div_(self.ema_weights.sum() + 1e-9)
+    
+                return weights_for_forward
+    
+            # During inference, return the frozen stable weights
+            return self.ema_weights
+        else:
+            num_tokens = hidden_states.shape[0]
+    
+            # if self.training and self.jitter_noise > 0:
+            #     noise = torch.randn_like(hidden_states) * self.jitter_noise
+            #     hidden_states = hidden_states + noise
+    
+            logits = self.layer(hidden_states)
+            probs = F.softmax(logits, dim=-1, dtype=torch.float32)
+            topk_probs, topk_indices = torch.topk(probs, k=self.k, dim=-1)
+            topk_probs_normalized = topk_probs / torch.sum(topk_probs, dim=-1, keepdim=True)
+            
+            # Load Balancing for K = 1
+            
+            # flat_topk_indices = topk_indices.flatten()
+            # one_hot_assignments = F.one_hot(flat_topk_indices, num_classes=self.num_experts).float()
+            # tokens_per_expert_fraction = one_hot_assignments.sum(0) / num_tokens
+            # print(tokens_per_expert_fraction)
+            # router_prob_per_expert = torch.mean(probs, dim=0)
+    
+            # Load Balancing for K > 1
+            one_hot = F.one_hot(topk_indices, num_classes=self.num_experts).float()  
+            tokens_per_expert = torch.sum(one_hot * topk_probs.unsqueeze(-1), dim=(0, 1)) / num_tokens
+            router_prob_per_expert = torch.mean(probs, dim=0)
+        
+            aux_loss = self.num_experts * torch.mean(tokens_per_expert * router_prob_per_expert)
+    
+            # --- DEBUG: print expert utilization for this batch ---
+            # print("Expert utilization (fraction of tokens per expert):", tokens_per_expert.detach().cpu().numpy())
+            # print(aux_loss)
+    
+            return topk_indices, topk_probs_normalized, aux_loss
+
+# --- 2. The MoE Layer (Minor cleanup for debug prints) ---
+class LoraMoELayerTopK(nn.Module):
+    """
+    Implements the MoL layer with expert merging.
+    Allows for efficient dense computation by collapsing experts 
+    into a single adapter based on router weights.
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        
+        dim = config.hidden_size
+        expert_intermediate_dim = config.expert_intermediate_size
+        num_experts = config.num_experts
+        lora_rank = config.lora_rank
+        
+        self.k = config.top_k
+        self.num_experts = num_experts
+        
+        self.norm = nn.LayerNorm(dim, eps=config.layer_norm_eps)
+        
+        self.router = SwitchRouterTopK(config)
+        self.shared_ffn = SharedLoraFFN(config)
+        
+        # The pool of Expert LoRA weights {\Delta_1, ..., \Delta_E}
+        self.lora_A1 = nn.Parameter(torch.randn(num_experts, lora_rank, dim))
+        self.lora_B1 = nn.Parameter(torch.zeros(num_experts, expert_intermediate_dim, lora_rank))
+        self.lora_A2 = nn.Parameter(torch.randn(num_experts, lora_rank, expert_intermediate_dim))
+        self.lora_B2 = nn.Parameter(torch.zeros(num_experts, dim, lora_rank))
+        
+        # Initialization (Kaiming Uniform)
+        for i in range(num_experts):
+            nn.init.kaiming_uniform_(self.lora_A1[i], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.lora_A2[i], a=math.sqrt(5))
+
+    def forward(self, hidden_states: torch.Tensor):
+        if self.config.routing_strategy == "ema":
+            residual = hidden_states
+            hidden_states_norm = self.norm(hidden_states)
+            
+            # 1. Get the global merging weights (w_t)
+            # Returns shape: (num_experts,)
+            merge_weights = self.router(hidden_states_norm)
+            
+            # 2. Weighted Merge of all LoRA parameters
+            # Formula: \Delta_{merged} = \sum_{j=1}^E w_j * \Delta_j
+            # We reshape weights to [Experts, 1, 1] for broadcasting against [Experts, Rank, Dim]
+            w = merge_weights.view(-1, 1, 1)
+            
+            merged_A1 = torch.sum(w * self.lora_A1, dim=0)
+            merged_B1 = torch.sum(w * self.lora_B1, dim=0)
+            merged_A2 = torch.sum(w * self.lora_A2, dim=0)
+            merged_B2 = torch.sum(w * self.lora_B2, dim=0)
+    
+            # 3. Dense Forward Pass
+            # Pass the merged adapter to the FFN. 
+            output = self.shared_ffn(
+                hidden_states_norm, 
+                merged_A1, merged_B1, 
+                merged_A2, merged_B2
+            )
+    
+            # We return 0.0 for aux_loss because we are not doing load balancing in this mode
+            return residual + output, torch.tensor(0.0, device=hidden_states.device)
+        elif self.config.routing_strategy == "uniform":
+            residual = hidden_states
+            hidden_states_norm = self.norm(hidden_states)
+            
+            # 1. Get the global merging weights (w_t)
+            # Returns shape: (num_experts,)
+            merge_weights = torch.ones(self.config.num_experts, dtype=hidden_states_norm.dtype, device=hidden_states_norm.device) / (self.config.num_experts)
+            
+            # 2. Weighted Merge of all LoRA parameters
+            # Formula: \Delta_{merged} = \sum_{j=1}^E w_j * \Delta_j
+            # We reshape weights to [Experts, 1, 1] for broadcasting against [Experts, Rank, Dim]
+            w = merge_weights.view(-1, 1, 1)
+            
+            merged_A1 = torch.sum(w * self.lora_A1, dim=0)
+            merged_B1 = torch.sum(w * self.lora_B1, dim=0)
+            merged_A2 = torch.sum(w * self.lora_A2, dim=0)
+            merged_B2 = torch.sum(w * self.lora_B2, dim=0)
+    
+            # 3. Dense Forward Pass
+            # Pass the merged adapter to the FFN. 
+            output = self.shared_ffn(
+                hidden_states_norm, 
+                merged_A1, merged_B1, 
+                merged_A2, merged_B2
+            )
+    
+            # We return 0.0 for aux_loss because we are not doing load balancing in this mode
+            return residual + output, torch.tensor(0.0, device=hidden_states.device)
+        else:
+            residual = hidden_states
+            hidden_states_norm = self.norm(hidden_states)
+            num_tokens, dim = hidden_states_norm.shape
+    
+            topk_indices, topk_probs, aux_loss = self.router(hidden_states_norm)
+            
+            # Efficient permutation-based dispatch
+            flat_token_indices = torch.arange(num_tokens, device=hidden_states.device).repeat_interleave(self.k)
+            flat_expert_indices = topk_indices.flatten()
+            
+            perm_indices = torch.argsort(flat_expert_indices)
+            sorted_token_indices = flat_token_indices[perm_indices]
+            sorted_expert_indices = flat_expert_indices[perm_indices]
+            
+            permuted_tokens = hidden_states_norm[sorted_token_indices]
+            permuted_probs = topk_probs.flatten()[perm_indices]
+    
+            tokens_per_expert = F.one_hot(sorted_expert_indices, self.num_experts).sum(dim=0)
+            split_tokens = torch.split(permuted_tokens, tokens_per_expert.tolist(), dim=0)
+            split_probs = torch.split(permuted_probs, tokens_per_expert.tolist(), dim=0)
+    
+            # Batched processing loop over experts
+            expert_outputs = []
+            for i in range(self.num_experts):
+                if tokens_per_expert[i] > 0:
+                    output = self.shared_ffn(
+                        split_tokens[i],
+                        self.lora_A1[i], self.lora_B1[i],
+                        self.lora_A2[i], self.lora_B2[i]
+                    )
+                    expert_outputs.append(output * split_probs[i].unsqueeze(1))
+                else:
+                    expert_outputs.append(torch.empty(0, dim, device=hidden_states.device))
+    
+            # Un-permute and combine results
+            concatenated_outputs = torch.cat(expert_outputs, dim=0)
+            inverse_perm_indices = torch.argsort(perm_indices)
+            unpermuted_outputs = concatenated_outputs[inverse_perm_indices]
+            
+            final_output = unpermuted_outputs.view(num_tokens, self.k, dim).sum(dim=1)
+            
+            # Final residual connection
+            output = residual + final_output
+            return output, aux_loss
+
+        
+class ModernAlbertMLP(nn.Module):
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__()
+        self.config = config
+        self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=False)
+        # self.act = ACT2FN[config.hidden_activation]
+        self.act = ACT2FN["gelu"]
+        self.drop = nn.Dropout(config.hidden_dropout_prob)
+        self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
+        return self.Wo(self.drop(self.act(input) * gate))
+
+#Flash Attention Rotatory Embedding
+class ApplyRotaryEmbUnpad(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        qkv,
+        cos,
+        sin,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+    ):
+        # (total_nnz, 3, nheads, headdim)
+        qkv = qkv.contiguous()
+        total_nnz, _three, _nheads, headdim = qkv.shape
+        # We need qkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+        # we get the same tensor
+        # qk = rearrange(qkv[:, :2], "b_s t h d -> b_s (t h) d")
+        qk = qkv[:, :2].view(total_nnz, -1, headdim)
+        apply_rotary(
+            qk,
+            cos,
+            sin,
+            seqlen_offsets=0,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            interleaved=False,
+            inplace=True,
+        )
+
+        ctx.save_for_backward(cos, sin, cu_seqlens)
+        ctx.max_seqlen = max_seqlen
+        return qkv
+
+    @staticmethod
+    def backward(ctx, do):
+        cos, sin, cu_seqlens = ctx.saved_tensors
+        do = do.contiguous()
+        total_nnz, _three, _nheads, headdim = do.shape
+        # We need dqkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+        # we get the same tensor
+        dqk = do[:, :2].view(total_nnz, -1, headdim)
+        apply_rotary(
+            dqk,
+            cos,
+            sin,
+            seqlen_offsets=0,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=ctx.max_seqlen,
+            interleaved=False,
+            inplace=True,
+            conjugate=True,
+        )
+
+        return do, None, None, None, None, None, None
+
+
+def apply_rotary_unpadded(
+    qkv,
+    cos,
+    sin,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[int] = None,
+):
+    """
+    Arguments:
+        qkv: (total_nnz, 3, nheads, headdim) - input tensor for packed QKV.
+        cos, sin: (seqlen_rotary, rotary_dim / 2)
+        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
+            of 1st half and 2nd half (GPT-NeoX style).
+        inplace: if True, apply rotary embedding in-place.
+        seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+        cu_seqlens: (batch + 1,) or None
+        max_seqlen: int
+    Return:
+        out: (total_nnz, dim)
+    rotary_dim must be <= headdim
+    Apply rotary embedding to the first rotary_dim of x.
+    """
+    return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)
+
+
+class ModernAlbertUnpaddedRotaryEmbedding(RotaryEmbedding):
+    """
+    The rotary position embeddings applied directly to unpadded sequences.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        base: float = 10000.0,
+        max_seqlen: Optional[int] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        """
+        max_seqlen: if max_seqlen, device, and dtype are provided, we precompute the cos_sin_cache
+            up to max_seqlen. If the max_seqlen, device, or dtype during training/inference differ,
+            the cos_sin_cache will be recomputed during the forward pass.
+        """
+        super().__init__(dim=dim, base=base, device=device, interleaved=False)
+        self.max_seqlen = max_seqlen
+
+        if max_seqlen is not None and device is not None and dtype is not None:
+            self._update_cos_sin_cache(max_seqlen, device=device, dtype=dtype)
+
+    def forward(
+        self,
+        qkv: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: Optional[int] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Apply rotary embedding *inplace* to qkv.
+        qkv: (total_nnz, 3, nheads, headdim)
+        cu_seqlens: (batch + 1,) cumulative sequence lengths
+        max_seqlen: int max seq length in the batch
+        """
+        if max_seqlen is not None:
+            self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
+
+        qkv = apply_rotary_unpadded(
+            qkv,
+            self._cos_cached,
+            self._sin_cached,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+
+        return qkv
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, base={self.base}, scale_base={self.scale_base}"
+
+class ModernAlbertRotaryEmbedding(nn.Module):
+    def __init__(self, config: ModernALBERTConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+# GeGLU unchanged
+class GeGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.w1 = nn.Linear(dim_in, dim_out)
+        self.w2 = nn.Linear(dim_in, dim_out)
+    def forward(self, x):
+        return F.gelu(self.w1(x)) * self.w2(x)
+
+
+#Flash Attention
+def _unpad_modernbert_input(
+    inputs: torch.Tensor,
+    attention_mask: torch.Tensor,
+    position_ids: Optional[torch.Tensor] = None,
+    labels: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
+    """
+    Remove padding from input sequences.
+
+    Args:
+        inputs: (batch, seqlen, ...) or (batch, seqlen)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+        position_ids: (batch, seqlen), int, position ids
+        labels: (batch, seqlen), int, labels
+
+    Returns:
+        unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
+        indices: (total_nnz)
+        cu_seqlens: (batch + 1), the cumulative sequence lengths
+        max_seqlen_in_batch: int
+        unpadded_position_ids: (total_nnz) or None
+        unpadded_labels: (total_nnz) or None
+    """
+    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 = int(seqlens_in_batch.max().item())
+    cu_seqlens = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+
+    if inputs.dim() == 2:
+        unpadded_inputs = inputs.flatten()[indices]
+    else:
+        batch, seqlen, *rest = inputs.shape
+        shape = batch * seqlen
+        unpadded_inputs = inputs.view(shape, *rest)[indices]
+
+    unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
+    unpadded_labels = labels.flatten()[indices] if labels is not None else None
+
+    return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels
+
+
+def _pad_modernbert_output(
+    inputs: torch.Tensor,
+    indices: torch.Tensor,
+    batch: int,
+    seqlen: int,
+) -> torch.Tensor:
+    """
+    Add padding to sequences.
+
+    Args:
+        inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
+        indices: (total_nnz)
+        batch: int, batch size
+        seqlen: int, max sequence length
+
+    Returns:
+        padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
+    """
+    if inputs.dim() == 1:
+        output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
+        output[indices] = inputs
+        padded_inputs = output.view(batch, seqlen)
+    else:
+        _, *rest = inputs.shape
+        output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
+        output[indices] = inputs
+        padded_inputs = output.view(batch, seqlen, *rest)
+
+    return padded_inputs
+
+
+def flash_attention_forward(
+    module: "SharedGroup",
+    qkv: torch.Tensor,
+    rotary_emb: ModernAlbertUnpaddedRotaryEmbedding,
+    cu_seqlens: torch.Tensor,
+    max_seqlen: int,
+    local_attention: tuple[int, int],
+    bs: int,
+    dim: int,
+    target_dtype: torch.dtype = torch.bfloat16,
+    **_kwargs,
+) -> tuple[torch.Tensor]:
+    # (total_seqlen, 3, nheads, headdim)
+    qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
+
+    convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
+    if convert_dtype:
+        # FA2 implementation only supports fp16 and bf16. If FA2 is supported,
+        # bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
+        orig_dtype = qkv.dtype
+        qkv = qkv.to(target_dtype)
+
+        attn = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            dropout_p=module.att_dropout.p if module.training else 0.0,
+            # deterministic=module.deterministic_flash_attn,
+            # deterministic=False,
+            window_size=local_attention,
+        )
+        attn = attn.to(orig_dtype)  # type: ignore
+    else:
+        attn = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            dropout_p=module.att_dropout.p if module.training else 0.0,
+            # deterministic=module.deterministic_flash_attn,
+            window_size=local_attention,
+        )
+    return (attn.view(bs, dim),)
+
+def sdpa_attention_forward(
+    module: "SharedGroup",
+    qkv: torch.Tensor,
+    attention_mask: torch.Tensor,
+    sliding_window_mask: torch.Tensor,
+    position_ids: Optional[torch.LongTensor],
+    local_attention: tuple[int, int],
+    bs: int,
+    dim: int,
+    **_kwargs,
+) -> tuple[torch.Tensor]:
+    # qkv: [batch_size, seqlen, 3, nheads, headdim]
+    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+    # query, key, value: [batch_size, heads, seq_len, head_dim]
+    query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+    if local_attention != (-1, -1):
+        attention_mask = sliding_window_mask
+
+    attn_output = (
+        F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            dropout_p=module.attention_dropout.p if module.training else 0.0,
+            attn_mask=attention_mask,
+        )
+        .transpose(1, 2)
+        .contiguous()
+    )
+    attn_output = attn_output.view(bs, -1, dim)
+    return (attn_output,)
+
+class SharedGroup(nn.Module):
+    def __init__(self, config): # config: ModernALBERTConfig
+        super().__init__()
+        self.config = config
+    
+        hs, nh = config.hidden_size, config.num_attention_heads
+        self.head_dim = hs // nh
+        self.num_heads = nh
+        self.use_adapter = config.use_adapter
+        eps = config.layer_norm_eps
+
+        rope_theta = 10000
+        
+        # Norms
+        self.att_pre_norm = nn.LayerNorm(hs, eps=eps)
+        self.ffn_pre_norm = nn.LayerNorm(hs, eps=eps)
+        
+        # Attention
+        self.qkv = nn.Linear(hs, 3 * hs)
+        self.out_proj = nn.Linear(hs, hs)
+        self.att_dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.local_attention = (-1, -1)
+        
+        if FLASH_ATTN_AVAILABLE:
+            self.rotary_emb = ModernAlbertUnpaddedRotaryEmbedding(
+                dim=self.head_dim, max_seqlen=config.max_position_embeddings, base=rope_theta
+            )
+        else:
+            config_copy = copy.deepcopy(config)
+            config_copy.rope_theta = rope_theta
+            self.rotary_emb = ModernAlbertRotaryEmbedding(config=config_copy)
+            
+        # FFN
+        self.mlp = ModernAlbertMLP(config)
+
+    def forward(self, inputs, mask, config, start_idx=0, use_moa=False, **kwargs):        
+        outputs = [] if config.output_hidden_states else None
+        attn_maps = [] if config.output_attentions else None
+
+        x = inputs
+
+        for i in range(config.group_depth):
+            h = x
+            h_norm = self.att_pre_norm(h)
+            
+            qkv_proj = self.qkv(h_norm)           
+            bs = h.shape[0]
+
+            # --- Attention Calculation ---
+            if FLASH_ATTN_AVAILABLE:
+                qkv = qkv_proj.view(-1, 3, self.num_heads, self.head_dim)
+        
+                attn_outputs = flash_attention_forward(
+                    self,
+                    qkv=qkv,
+                    rotary_emb=self.rotary_emb,
+                    local_attention=self.local_attention,
+                    bs=bs,
+                    dim=self.head_dim * self.num_heads,
+                    **kwargs,
+                )
+                
+                attn_out = attn_outputs[0]
+            else: # Fallback to PyTorch Scaled Dot Product Attention
+                qkv = qkv_proj.view(bs, -1, 3, self.num_heads, self.head_dim)
+                attn_mask = mask[:, None, None, :]
+                
+                attn_outputs = sdpa_attention_forward(
+                    self,
+                    qkv=qkv,
+                    rotary_emb=self.rotary_emb,
+                    local_attention=self.local_attention,
+                    bs=bs,
+                    dim=self.head_dim * self.num_heads,
+                    **kwargs,
+                )
+                
+                attn_out = attn_outputs[0]
+
+            x = self.att_dropout(self.out_proj(attn_out)) + h
+
+            if use_moa == True and i == config.group_depth - 1:
+                return x, outputs, attn_maps
+            else:
+                # FFN block
+                h2 = x
+                h2_norm = self.ffn_pre_norm(h2)
+                    
+                x = self.mlp(h2_norm) + h2
+                
+                # Collect hidden state if needed
+                if config.output_hidden_states:
+                    outputs.append(x)
+                
+        return x, outputs, attn_maps
+
+
+class ModernAlbertEmbeddings(nn.Module):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__()
+        self.config = config
+        
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
+        self.embed_proj = nn.Linear(config.embedding_size, config.hidden_size)
+        
+        # self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)
+        self.drop = nn.Dropout(0.0)
+
+    def forward(
+        self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        if inputs_embeds is not None:
+            hidden_states = self.drop(self.norm(self.embed_proj(inputs_embeds)))
+        else:
+            hidden_states = self.drop(self.norm(self.embed_proj(self.tok_embeddings(input_ids))))
+        
+        return hidden_states
+
+@dataclass
+class MoABaseModelOutput(BaseModelOutput):
+    load_balancing_loss: Optional[torch.FloatTensor] = None
+
+class ModernALBERTModel(ts.PreTrainedModel):
+    config_class = ModernALBERTConfig
+    base_model_prefix = "modernAlbert"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__(config)
+        self.config = config
+        
+        # Factorized embeddings
+        self.embeddings = ModernAlbertEmbeddings(config)
+        self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)
+        self.num_groups = config.num_hidden_layers // config.group_depth
+        self.groups = nn.ModuleList([SharedGroup(config) for _ in range(self.num_groups)])
+        
+        if config.use_moa:
+            self.moa_layers = nn.ModuleList([
+                # LoraMoELayerTopK(config) for _ in range(self.num_groups - 1) 
+                LoraMoELayerTopK(config) for _ in range(config.num_expert_modules) 
+            ])
+            
+        self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
+        self.post_init()
+
+    def forward(self, 
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        all_hidden_states = []
+        
+        if batch_size is None and seq_len is None:
+            if inputs_embeds is not None:
+                batch_size, seq_len = inputs_embeds.shape[:2]
+            else:
+                batch_size, seq_len = input_ids.shape[:2]
+        
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        if output_hidden_states:
+            self.config.output_hidden_states = True
+
+        hs, atts = ([] if output_hidden_states else None), ([] if output_attentions else None)
+        all_aux_losses = []
+        
+        repad = False
+        if FLASH_ATTN_AVAILABLE:
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                repad = True
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask
+                    )
+        else:
+            if position_ids is None:
+                position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
+
+            attention_mask, sliding_window_mask = self._update_attention_mask(
+                attention_mask, output_attentions=output_attentions
+            )
+
+        hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)        
+        x = hidden_states
+
+        if output_hidden_states:
+            hs.append(x)
+
+        # Mask
+        mask = None
+        if attention_mask is not None:
+            mask = attention_mask.to(torch.bool)
+        
+        for i, group in enumerate(self.groups):
+
+            is_moa = self.config.use_moa and (i > len(self.groups) - len(self.moa_layers) - 1)
+            moa_idx = i - (len(self.groups) - len(self.moa_layers))
+            
+            x, layer_hs, layer_atts = group(x, 
+                                            mask, 
+                                            self.config,             
+                                            sliding_window_mask=sliding_window_mask,
+                                            position_ids=position_ids,
+                                            cu_seqlens=cu_seqlens,
+                                            max_seqlen=max_seqlen,
+                                            use_moa=is_moa,
+                                            output_attentions=output_attentions,)
+            
+            if output_hidden_states and layer_hs:
+                hs.extend(layer_hs)
+            if output_attentions and layer_atts:
+                atts.extend(layer_atts)
+
+            # After each group (except the last), apply the MoA layer
+            if self.config.use_moa and is_moa:
+                x, aux_loss = self.moa_layers[moa_idx](x)
+                if output_hidden_states:
+                    hs.append(x)
+                all_aux_losses.append(aux_loss)
+
+
+        hidden_states = self.final_norm(x)
+
+        # hidden_states = _pad_modernbert_output(
+        #         inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
+        # )
+        
+        if repad:
+            hidden_states = _pad_modernbert_output(
+                inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
+            )
+            if all_hidden_states is not None:
+                all_hidden_states = tuple(
+                    _pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
+                    for hs in all_hidden_states
+                )
+
+
+        load_balancing_loss = None
+        if all_aux_losses != []:
+            load_balancing_loss = torch.stack(all_aux_losses).mean() * self.config.load_balancing_loss_coef
+
+        return MoABaseModelOutput(last_hidden_state=hidden_states, hidden_states=hs, attentions=atts, load_balancing_loss=load_balancing_loss)
+
+class ModernAlbertPredictionHead(nn.Module):
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__()
+        self.config = config
+        self.dense = nn.Linear(config.hidden_size, config.embedding_size, bias=False)
+        self.act = ACT2FN["gelu"]
+        self.norm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.norm(self.act(self.dense(hidden_states)))
+
+class ModernALBERTForMaskedLM(ts.PreTrainedModel):
+    """
+    Modern ALBERT model with a Masked Language Modeling (MLM) head,
+    optimized to mirror the HuggingFace `AlbertForMaskedLM` API.
+    """
+    _tied_weights_keys = ["decoder.weight"]
+    config_class = ModernALBERTConfig
+    base_model_prefix = "modernAlbert"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__(config)
+        self.config = config
+
+        # Base encoder without pooling
+        self.albert = ModernALBERTModel(config)
+
+        # MLM head
+        self.head = ModernAlbertPredictionHead(config)
+        self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.albert.embeddings.tok_embeddings
+
+    def get_output_embeddings(self):
+        return self.decoder
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear):
+        self.decoder = new_embeddings
+
+    @torch.compile(dynamic=True)
+    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.head(output))
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        if FLASH_ATTN_AVAILABLE:
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+                if attention_mask is None:
+                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                    )
+                    
+        # Encode
+        outputs = self.albert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = outputs[0]
+
+        if FLASH_ATTN_AVAILABLE:
+            last_hidden_state_unpaded = _pad_modernbert_output(inputs=last_hidden_state, indices=indices, batch=batch_size, seqlen=seq_len)
+            if outputs.hidden_states != None:
+                outputs.hidden_states.append(last_hidden_state_unpaded)
+        
+        logits = self.decoder(self.head(last_hidden_state))
+        
+        loss = None
+        
+        if labels is not None:
+            loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size, **kwargs)
+
+            if outputs.load_balancing_loss != None and self.training:
+                # print(outputs.load_balancing_loss)
+                loss += outputs.load_balancing_loss
+            
+        if FLASH_ATTN_AVAILABLE:
+            logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+class ModernALBERTForSequenceClassification(ts.PreTrainedModel):
+    config_class = ModernALBERTConfig
+    
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.albert = ModernALBERTModel(config)
+        self.head = ModernAlbertPredictionHead(config)
+        self.drop = torch.nn.Dropout(0.0)
+        self.classifier = nn.Linear(config.embedding_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # self._maybe_set_compile()
+
+        if FLASH_ATTN_AVAILABLE:
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+                if attention_mask is None:
+                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, _ = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=None
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, _ = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=None
+                    )
+        
+        outputs = self.albert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            # sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        
+        last_hidden_state = outputs[0]
+        last_hidden_state = _pad_modernbert_output(inputs=last_hidden_state, indices=indices, batch=batch_size, seqlen=seq_len)
+
+        # if self.config.classifier_pooling == "cls":
+        # last_hidden_state = last_hidden_state[:, 0]
+        # elif self.config.classifier_pooling == "mean":
+        last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+            dim=1, keepdim=True
+        )
+
+        pooled_output = self.head(last_hidden_state)
+        pooled_output = self.drop(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            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(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+class ModernALBERTForQuestionAnswering(ts.PreTrainedModel):
+    config_class = ModernALBERTConfig
+
+    def __init__(self, config: ModernALBERTConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.albert = ModernALBERTModel(config)
+        self.head = ModernAlbertPredictionHead(config)
+        self.drop = torch.nn.Dropout(0.0)
+        self.classifier_head = nn.Linear(config.embedding_size, config.num_labels)
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # self._maybe_set_compile()
+
+        outputs = self.albert(
+            input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        last_hidden_state = self.head(last_hidden_state)
+        last_hidden_state = self.drop(last_hidden_state)
+        logits = self.classifier_head(last_hidden_state)
+
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# Distillation
+
+@dataclass
+class DistillationOutputWithPasts(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+    depth_loss: Optional[torch.FloatTensor] = None
+    audio_logits: torch.FloatTensor = None
+    depth_past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    depth_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    depth_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+
+class DistillationWrapper(ts.PreTrainedModel):
+    config_class = ModernALBERTConfig
+    base_model_prefix = "model"    
+    _no_split_modules = ["LlamaDecoderLayer", "FlowDecoderLayerGroup", "MimiTransformerLayer"]
+    _keys_to_ignore_on_load_missing = ["speech_tokenizer", "teacher"]
+    _tied_weights_keys = ["llm.decoder.weight"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _tp_plan = []
+
+    def __init__(self, config, student=None, teacher=None):
+        super().__init__(config)
+        self.llm = ModernALBERTForMaskedLM(config)
+
+        if teacher != None:
+            self.teacher = teacher
+        else:
+            self.teacher = None
+    
+        self.attention_loss = nn.KLDivLoss(reduction="mean")
+        self.hidden_loss = nn.CosineEmbeddingLoss(reduction="mean")
+        self.output_loss = nn.KLDivLoss(reduction="batchmean")
+
+        self.temperature = 1.0
+
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
+
+        device = self.device
+
+        
+
+        input_ids = input_ids.to(device)
+
+        attention_mask = attention_mask.to(device)
+
+
+
+        if labels != None:
+
+            labels = labels.to(device)
+
+
+
+        student_outputs = self.llm(
+
+            input_ids=input_ids,
+
+            attention_mask=attention_mask,
+
+            labels=labels,
+
+            output_hidden_states=True,
+
+            # output_attentions=True,
+
+            **kwargs
+
+        )
+
+
+
+        hidden_loss = None
+
+        output_loss = None
+
+        
+
+        if self.teacher != None:
+
+            with torch.no_grad():
+
+              input_ids = input_ids.to(self.teacher.device)
+
+              attention_mask = attention_mask.to(self.teacher.device)
+
+            
+
+              teacher_outputs = self.teacher(input_ids=input_ids,
+
+                                             attention_mask=attention_mask,
+
+                                             output_hidden_states=True,
+
+                                            #  output_attentions=True,
+
+                                             **kwargs
+
+              )
+
+
+
+            s_hiddens = student_outputs.hidden_states[-1]
+
+            t_hiddens = teacher_outputs.hidden_states[-1].detach()
+
+
+
+            s_logits = student_outputs.logits
+
+            t_logits = teacher_outputs.logits.detach()
+
+            
+
+            hidden_loss = self.compute_hidden_loss(s_hiddens, t_hiddens, attention_mask)
+
+            output_loss = self.compute_output_loss(s_logits, t_logits, labels)
+
+
+
+        if self.teacher != None:
+
+            total_loss = (1.0 * student_outputs.loss) + (3.0 * hidden_loss) + (5.0 * output_loss)
+
+        else:
+
+            total_loss = student_outputs.loss
+
+
+
+        return DistillationOutputWithPasts(
+
+            loss=total_loss,
+
+            logits=student_outputs.logits,
+
+            hidden_states=student_outputs.hidden_states,
+
+            attentions=student_outputs.attentions,
+
+        )
+
+
+
+    def compute_output_loss(self, s_logits, t_logits, labels):
+
+        mask = (labels > -1).unsqueeze(-1)  
+
+        s_logits_masked = s_logits.masked_fill(~mask, 0.0)
+
+        t_logits_masked = t_logits.masked_fill(~mask, 0.0)
+
+
+
+        s_logits_slct = s_logits_masked.view(-1, s_logits.size(-1))
+
+        t_logits_slct = t_logits_masked.view(-1, t_logits.size(-1))
+
+        
+
+        valid_rows = mask.view(-1)
+
+        s_logits_slct = s_logits_slct[valid_rows]
+
+        t_logits_slct = t_logits_slct[valid_rows]
+
+
+
+        output_loss = (
+
+            self.output_loss(
+
+                nn.functional.log_softmax(s_logits_slct / self.temperature, dim=-1),
+
+                nn.functional.softmax(t_logits_slct / self.temperature, dim=-1),
+
+            )
+
+            * (self.temperature) ** 2
+
+        )
+
+    
+
+        return output_loss
+
+
+
+    def compute_hidden_loss(self, s_hiddens, t_hiddens, attention_mask, lambdas=None):                
+
+        s_hidden_states = s_hiddens
+
+        t_hidden_states = t_hiddens 
+
+        
+
+        assert s_hidden_states.size() == t_hidden_states.size()
+
+        
+
+        dim = s_hidden_states.size(-1)
+
+        s_hidden_states_slct = s_hidden_states
+
+        t_hidden_states_slct = t_hidden_states
+
+        
+
+        target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1)  # (bs * seq_length,)
+
+    
+
+        hidden_loss = self.hidden_loss(s_hidden_states_slct, t_hidden_states_slct, target)
+
+        
+
+        return hidden_loss
+
+
+class DistillationWrapperForSequenceClassification(ts.PreTrainedModel):
+    config_class = ModernALBERTConfig
+    base_model_prefix = "model"    
+    _no_split_modules = ["LlamaDecoderLayer", "FlowDecoderLayerGroup", "MimiTransformerLayer"]
+    _keys_to_ignore_on_load_missing = ["speech_tokenizer", "teacher"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.llm = ModernALBERTForSequenceClassification(config)
+
+        self.teacher = None
+        
+        self.attention_loss = nn.KLDivLoss(reduction="mean")
+        self.hidden_loss = nn.CosineEmbeddingLoss(reduction="mean")
+        self.output_loss = nn.KLDivLoss(reduction="batchmean")
+
+        self.temperature = 1.0
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
+        device = self.device
+        
+        input_ids = input_ids.to(device)
+        attention_mask = attention_mask.to(device)
+
+        if labels != None:
+            labels = labels.to(device)
+
+        student_outputs = self.llm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            output_hidden_states=True,
+            # output_attentions=True,
+            # **kwargs
+        )
+
+        hidden_loss = None
+        output_loss = None
+        
+        if self.teacher != None:
+            with torch.no_grad():
+              input_ids = input_ids.to(self.teacher.device)
+              attention_mask = attention_mask.to(self.teacher.device)
+            
+              teacher_outputs = self.teacher(input_ids=input_ids,
+                                             attention_mask=attention_mask,
+                                             output_hidden_states=True,
+                                            #  output_attentions=True,
+                                            #  **kwargs)
+              )
+
+            s_hiddens = student_outputs.hidden_states[-1]
+            t_hiddens = teacher_outputs.hidden_states[-1].detach()
+
+            # print(s_hiddens.shape)
+            # print(t_hiddens.shape)
+        
+            s_logits = student_outputs.logits
+            t_logits = teacher_outputs.logits.detach()
+            
+            hidden_loss = self.compute_hidden_loss(s_hiddens, t_hiddens, attention_mask)
+            output_loss = self.compute_output_loss(s_logits, t_logits, labels)
+
+        if self.teacher != None:
+            total_loss = (1.0 * student_outputs.loss) + (3.0 * hidden_loss) + (5.0 * output_loss)
+        else:
+            total_loss = student_outputs.loss
+
+        return DistillationOutputWithPasts(
+            loss=total_loss,
+            logits=student_outputs.logits,
+            hidden_states=student_outputs.hidden_states,
+            attentions=student_outputs.attentions,
+        )
+
+    def compute_output_loss(self, s_logits, t_logits, labels):
+        mask = (labels > -1).unsqueeze(-1).expand_as(s_logits).bool()
+    
+        s_logits_slct = torch.masked_select(s_logits, mask)  
+        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))  
+        t_logits_slct = torch.masked_select(t_logits, mask)  
+        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1)) 
+        assert t_logits_slct.size() == s_logits_slct.size()
+        
+        output_loss = (
+            self.output_loss(
+                nn.functional.log_softmax(s_logits_slct / self.temperature, dim=-1),
+                nn.functional.softmax(t_logits_slct / self.temperature, dim=-1),
+            )
+            * (self.temperature) ** 2
+        )
+    
+        return output_loss
+
+    def compute_hidden_loss(self, s_hiddens, t_hiddens, attention_mask, lambdas=None):                
+        s_hidden_states = s_hiddens
+        t_hidden_states = t_hiddens 
+    
+        # mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states).bool()  # (bs, seq_length, dim)
+    
+        assert s_hidden_states.size() == t_hidden_states.size()
+        
+        dim = s_hidden_states.size(-1)
+    
+        # s_hidden_states_slct = torch.masked_select(s_hidden_states, mask)  # (bs * seq_length * dim)
+        # s_hidden_states_slct = s_hidden_states_slct.view(-1, dim)  # (bs * seq_length, dim)
+        # t_hidden_states_slct = torch.masked_select(t_hidden_states, mask)  # (bs * seq_length * dim)
+        # t_hidden_states_slct = t_hidden_states_slct.view(-1, dim)  # (bs * seq_length, dim)
+
+        s_hidden_states_slct = s_hidden_states
+        t_hidden_states_slct = t_hidden_states
+        
+        target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1)  # (bs * seq_length,)
+    
+        hidden_loss = self.hidden_loss(s_hidden_states_slct, t_hidden_states_slct, target)
+        
+        return hidden_loss
+
+class DistillationWrapperForQuestionAnswering(ts.PreTrainedModel):
+    config_class = ModernALBERTConfig
+    base_model_prefix = "model"    
+    _no_split_modules = ["LlamaDecoderLayer", "FlowDecoderLayerGroup", "MimiTransformerLayer"]
+    _keys_to_ignore_on_load_missing = ["speech_tokenizer", "teacher"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.llm = ModernALBERTForQuestionAnswering(config)
+
+        self.teacher = None
+        
+        self.attention_loss = nn.KLDivLoss(reduction="mean")
+        self.hidden_loss = nn.CosineEmbeddingLoss(reduction="mean")
+        self.output_loss = nn.KLDivLoss(reduction="batchmean")
+
+        self.temperature = 1.0
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
+        device = self.device
+        
+        input_ids = input_ids.to(device)
+        attention_mask = attention_mask.to(device)
+
+        if labels != None:
+            labels = labels.to(device)
+
+        student_outputs = self.llm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            output_hidden_states=True,
+            # output_attentions=True,
+            # **kwargs
+        )
+
+        hidden_loss = None
+        output_loss = None
+        
+        if self.teacher != None:
+            with torch.no_grad():
+              input_ids = input_ids.to(self.teacher.device)
+              attention_mask = attention_mask.to(self.teacher.device)
+            
+              teacher_outputs = self.teacher(input_ids=input_ids,
+                                             attention_mask=attention_mask,
+                                             output_hidden_states=True,
+                                            #  output_attentions=True,
+                                            #  **kwargs)
+              )
+
+            s_hiddens = student_outputs.hidden_states[-1]
+            t_hiddens = teacher_outputs.hidden_states[-1].detach()
+
+            # print(s_hiddens.shape)
+            # print(t_hiddens.shape)
+        
+            s_logits = student_outputs.logits
+            t_logits = teacher_outputs.logits.detach()
+            
+            hidden_loss = self.compute_hidden_loss(s_hiddens, t_hiddens, attention_mask)
+            output_loss = self.compute_output_loss(s_logits, t_logits, labels)
+
+        if self.teacher != None:
+            total_loss = (1.0 * student_outputs.loss) + (3.0 * hidden_loss) + (5.0 * output_loss)
+        else:
+            total_loss = student_outputs.loss
+
+        # return DistillationOutputWithPasts(
+        #     loss=total_loss,
+        #     logits=student_outputs.logits,
+        #     hidden_states=student_outputs.hidden_states,
+        #     attentions=student_outputs.attentions,
+        # )
+
+        return student_outputs
+
+    def compute_output_loss(self, s_logits, t_logits, labels):
+        mask = (labels > -1).unsqueeze(-1).expand_as(s_logits).bool()
+    
+        s_logits_slct = torch.masked_select(s_logits, mask)  
+        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))  
+        t_logits_slct = torch.masked_select(t_logits, mask)  
+        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1)) 
+        assert t_logits_slct.size() == s_logits_slct.size()
+        
+        output_loss = (
+            self.output_loss(
+                nn.functional.log_softmax(s_logits_slct / self.temperature, dim=-1),
+                nn.functional.softmax(t_logits_slct / self.temperature, dim=-1),
+            )
+            * (self.temperature) ** 2
+        )
+    
+        return output_loss
+
+    def compute_hidden_loss(self, s_hiddens, t_hiddens, attention_mask, lambdas=None):                
+        s_hidden_states = s_hiddens
+        t_hidden_states = t_hiddens 
+    
+        # mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states).bool()  # (bs, seq_length, dim)
+    
+        assert s_hidden_states.size() == t_hidden_states.size()
+        
+        dim = s_hidden_states.size(-1)
+    
+        # s_hidden_states_slct = torch.masked_select(s_hidden_states, mask)  # (bs * seq_length * dim)
+        # s_hidden_states_slct = s_hidden_states_slct.view(-1, dim)  # (bs * seq_length, dim)
+        # t_hidden_states_slct = torch.masked_select(t_hidden_states, mask)  # (bs * seq_length * dim)
+        # t_hidden_states_slct = t_hidden_states_slct.view(-1, dim)  # (bs * seq_length, dim)
+
+        s_hidden_states_slct = s_hidden_states
+        t_hidden_states_slct = t_hidden_states
+        
+        target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1)  # (bs * seq_length,)
+    
+        hidden_loss = self.hidden_loss(s_hidden_states_slct, t_hidden_states_slct, target)
+        
+        return hidden_loss