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.gitattributes

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config.json

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+{
+  "architectures": [
+    "modeling_llama_butler.LlamaButlerForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_reduce_factor": 8,
+  "auto_map": {
+    "AutoConfig": "modeling_llama_butler.LlamaButlerConfig",
+    "AutoModel": "modeling_llama_butler.LlamaButlerForCausalLM",
+    "AutoModelForCausalLM": "modeling_llama_butler.LlamaButlerForCausalLM"
+  },
+  "bos_token_id": 128000,
+  "dDash": 16,
+  "eos_token_id": 128001,
+  "eval_llm_mode": "ExpPred",
+  "flash_attn": false,
+  "head_attn_reduce_factor": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intdim": 1024,
+  "intermediate_size": 8192,
+  "lookahead": 0,
+  "max_position_embeddings": 131072,
+  "min_sparse_index": 8,
+  "mlp_bias": false,
+  "model_type": "llama_butler",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "producer_frequency": 28,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "sliding_window": 128,
+  "tie_word_embeddings": true,
+  "token_sparse_method": "fixed_50pc",
+  "torch_dtype": "float32",
+  "train_headpredictor": false,
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

conversion.py

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+from transformers import LlamaForCausalLM, LlamaConfig, AutoTokenizer
+import torch
+import os
+
+# huggingface-cli download meta-llama/Llama-3.2-3B tokenizer_config.json --local-dir ./
+# huggingface-cli download meta-llama/Llama-3.2-3B tokenizer.json --local-dir ./
+# huggingface-cli download meta-llama/Llama-3.2-3B special_tokens_map.json --local-dir ./
+
+question = "A $y$-intercept is a point on the graph that lies on the $y$-axis, so $x = 0$. Hence, the number $y$-intercepts corresponds to the number of real solutions of the quadratic equation $y^2 - 4y - 1 = 0$. The discriminant of this quadratic equation is $(-4)^2 + 4 \cdot 1 \cdot (-1) = 20$, which is positive, so the quadratic has two distinct real roots. Therefore, the number of $y$-intercepts is $\boxed{2}$. \n  \n [asy] \n size(150); \n real ticklen=3; \n real tickspace=2; \n  \n real ticklength=0.1cm; \n real axisarrowsize=0.14cm; \n pen axispen=black+1.3bp; \n real vectorarrowsize=0.2cm; \n real tickdown=-0.5; \n real tickdownlength=-0.15inch; \n real tickdownbase=0.3; \n real wholetickdown=tickdown; \n void rr_cartesian_axes(real xleft, real xright, real ybottom, real ytop, real xstep=1, real ystep=1, bool \n  \n useticks=false, bool complexplane=false, bool usegrid=true) { \n  \n import graph; \n  \n real i; \n  \n if(complexplane) { \n  \n label('$\textnormal{Re}$',(xright,0),SE); \n  \n label('$\textnormal{Im}$',(0,ytop),NW); \n  \n } else { \n  \n label('$x$',(xright+0.4,-0.5)); \n  \n label('$y$',(-0.5,ytop+0.2)); \n  \n } \n  \n ylimits(ybottom,ytop); \n  \n xlimits( xleft, xright); \n  \n real[] TicksArrx,TicksArry; \n  \n for(i=xleft+xstep; i<xright; i+=xstep) { \n  \n if(abs(i) >0.1) { \n  \n TicksArrx.push(i); \n  \n } \n  \n } \n  \n for(i=ybottom+ystep; i<ytop; i+=ystep) { \n  \n if(abs(i) >0.1) { \n  \n TicksArry.push(i); \n  \n } \n  \n } \n  \n if(usegrid) {"    
+predictor_load_path = "/home/ya255/projects/TokenButler/expt_model/TrainTokenButler_42_finetune_None_None_500_llama_meta-llama_Llama-3.2-3B_L3_3B_2k.csv_L3_3B_2k_False_False_2000_False_redpajama_1024_1_1_20_0.001_1024/16_False_4_1000_ExpPred_fixed_40pc_True_False_0_None_False_False_4_8_2_16_1024_False_False_True_28_0.38571428571428584__best.pt"
+base_model_name = "meta-llama/Llama-3.2-3B"
+
+def get_producer_layers(model):
+    """
+    Traverses the model to find the producer layer (layer_idx=0).cc
+    """
+    producer_modules = []
+    for module in model.modules():
+        if module.__class__.__name__.endswith("AttentionExperimental") and module.layer_idx == 0:
+            producer_modules.append(module)
+    return producer_modules
+    
+# 1) Load the base model from HF
+base_model = LlamaForCausalLM.from_pretrained(base_model_name, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+inputs = tokenizer(question, return_tensors="pt")
+inputs = {k: v.to(base_model.device) for k, v in inputs.items()}
+question_length = inputs['attention_mask'].shape[1]
+
+with torch.no_grad():
+    base_output_ids = base_model.generate(
+        **inputs,
+        max_new_tokens=200,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.7,
+    )
+base_output_text = tokenizer.decode(base_output_ids[0][question_length:], skip_special_tokens=True)
+
+# Remove base model from GPU
+base_model_device = base_model.device
+base_model.to("cpu")
+base_state_dict = base_model.state_dict()
+del base_model
+torch.cuda.empty_cache()
+
+from modeling_llama_butler import LlamaButlerConfig, LlamaButlerForCausalLM
+butler_config = LlamaButlerConfig.from_pretrained('config.json')
+
+butler_model = LlamaButlerForCausalLM(butler_config)
+butler_model.load_state_dict(base_state_dict, strict=False)
+
+model_producer_layers = get_producer_layers(butler_model)
+producer_layer_weights = torch.load(predictor_load_path)
+for idx, producer_layer_weight in enumerate(producer_layer_weights):
+    try:
+        model_producer_layers[idx].load_state_dict(producer_layer_weight, strict=False)
+    except Exception as e:
+        print(f"Error loading producer layer {idx}: {e}")
+        print("\n\nContinuing... !! Bad Perf If Unintentional !!\n\n")
+
+
+butler_model.to(base_model_device)
+butler_model.eval()
+
+with torch.no_grad():
+    butler_output_ids = butler_model.generate(
+        **inputs,
+        max_new_tokens=200,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.7,
+    )
+
+butler_output_text = tokenizer.decode(butler_output_ids[0][question_length:], skip_special_tokens=True)
+
+print("\n=== Base Model Output (Newlines Removed For Brevity) ===\n")
+print(base_output_text.replace("\n", ""))
+print("\n")
+print("=== Butler Model Output (Newlines Removed For Brevity) ===\n")
+print(butler_output_text.replace("\n", ""))
+print("\n")
+
+OUTPUT_DIR = "."
+print(f"\nSaving final merged model to: {OUTPUT_DIR}")
+butler_model.save_pretrained(OUTPUT_DIR, safe_serialization=False)
+
+# tokenizer.save_pretrained(OUTPUT_DIR)
+print("\nAll done! The folder should now have `pytorch_model.bin` and the updated `config.json`.\n")

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "transformers_version": "4.48.3"
+}

modeling_llama_butler.py

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+import torch
+import torch.nn as nn
+from typing import Dict
+from transformers import LlamaForCausalLM, LlamaConfig
+from transformers.generation.utils import GenerationConfig
+import os
+import pdb
+import copy
+import math
+import numpy as np 
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+import gc
+
+import traceback
+import torch
+from torch import nn
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from torch.cuda.amp import autocast
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.models.llama.configuration_llama import LlamaConfig
+from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, LlamaAttention, apply_rotary_pos_emb
+
+from transformers.cache_utils import DynamicCache
+
+class PredictorDynamicCache(DynamicCache):
+    def __init__(self):
+        super().__init__()
+        self.predictor_primary_key: List[Optional[torch.Tensor]] = []
+        self.predictor_primary_value: List[Optional[torch.Tensor]] = []
+        self.predictor_importance_key: List[Optional[torch.Tensor]] = []
+
+    def update_predictor_primary(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Append or create the predictor's "primary" K/V states for `layer_idx`.
+
+        shape for key_states, value_states is typically [batch_size, num_heads, seq_len, head_dim].
+        """
+        # Extend the lists so that `predictor_primary_key[layer_idx]` and
+        # `predictor_primary_value[layer_idx]` exist.
+        self._ensure_list_capacity(
+            self.predictor_primary_key, layer_idx, fill=None
+        )
+        self._ensure_list_capacity(
+            self.predictor_primary_value, layer_idx, fill=None
+        )
+
+        # If this is the very first time we are updating that layer's predictor cache, just assign
+        if self.predictor_primary_key[layer_idx] is None:
+            self.predictor_primary_key[layer_idx] = key_states
+            self.predictor_primary_value[layer_idx] = value_states
+        else:
+            # Otherwise, concatenate along the seq_len dimension (=-2 or =2 depending on your shape).
+            self.predictor_primary_key[layer_idx] = torch.cat(
+                [self.predictor_primary_key[layer_idx], key_states], dim=2
+            )
+            self.predictor_primary_value[layer_idx] = torch.cat(
+                [self.predictor_primary_value[layer_idx], value_states], dim=2
+            )
+
+        return (
+            self.predictor_primary_key[layer_idx],
+            self.predictor_primary_value[layer_idx],
+        )
+
+    def update_predictor_importance(
+        self,
+        key_states: torch.Tensor,
+        layer_idx: int,
+    ) -> torch.Tensor:
+        """
+        Append or create the predictor's "importance" key for `layer_idx`.
+        """
+        self._ensure_list_capacity(
+            self.predictor_importance_key, layer_idx, fill=None
+        )
+
+        if self.predictor_importance_key[layer_idx] is None:
+            self.predictor_importance_key[layer_idx] = key_states
+        else:
+            self.predictor_importance_key[layer_idx] = torch.cat(
+                [self.predictor_importance_key[layer_idx], key_states], dim=2
+            )
+        return self.predictor_importance_key[layer_idx]
+
+    def crop(self, max_length: int):
+        super().crop(max_length)
+        # Now also crop predictor caches
+        for idx in range(len(self.predictor_primary_key)):
+            if self.predictor_primary_key[idx] is not None:
+                self.predictor_primary_key[idx] = self.predictor_primary_key[idx][..., :max_length, :]
+                self.predictor_primary_value[idx] = self.predictor_primary_value[idx][..., :max_length, :]
+
+        for idx in range(len(self.predictor_importance_key)):
+            if self.predictor_importance_key[idx] is not None:
+                self.predictor_importance_key[idx] = self.predictor_importance_key[idx][..., :max_length, :]
+
+        # Remember to adjust self._seen_tokens accordingly
+        self._seen_tokens = min(self._seen_tokens, max_length)
+
+    def batch_split(
+        self, full_batch_size: int, split_size: int, num_hidden_layers: int = None
+    ) -> List["PredictorDynamicCache"]:
+        # Use the base split logic for the standard K/V
+        base_splits = super().batch_split(full_batch_size, split_size, num_hidden_layers)
+        # `base_splits` is now a list of new DynamicCache objects. But we *actually*
+        # want them to be PredictorDynamicCache so we can store the predictor states.
+        # Easiest: we can cast and fill them. 
+        out: List[PredictorDynamicCache] = []
+
+        for split_i, base_split in enumerate(base_splits):
+            # Construct an empty PredictorDynamicCache
+            new_cache = PredictorDynamicCache()
+            # Copy over the underlying fields from base_split
+            new_cache.key_cache = base_split.key_cache
+            new_cache.value_cache = base_split.value_cache
+            new_cache._seen_tokens = base_split._seen_tokens
+
+            # Now also slice our predictor fields
+            # The slice in batch dim is [i:i+split_size].
+            b_start = split_i * split_size
+            b_end = min(full_batch_size, b_start + split_size)
+
+            new_cache.predictor_primary_key = self._slice_list_tensors(
+                self.predictor_primary_key, b_start, b_end
+            )
+            new_cache.predictor_primary_value = self._slice_list_tensors(
+                self.predictor_primary_value, b_start, b_end
+            )
+            new_cache.predictor_importance_key = self._slice_list_tensors(
+                self.predictor_importance_key, b_start, b_end
+            )
+
+            out.append(new_cache)
+
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["PredictorDynamicCache"], num_hidden_layers: int = None) -> "PredictorDynamicCache":
+        # Let the base class handle the normal K/V merges
+        base_merged = DynamicCache.from_batch_splits(splits, num_hidden_layers=num_hidden_layers)
+        merged = cls()
+        merged.key_cache = base_merged.key_cache
+        merged.value_cache = base_merged.value_cache
+        merged._seen_tokens = base_merged._seen_tokens
+
+        # Now unify predictor states by concatenating along batch dim=0
+        merged.predictor_primary_key = cls._merge_list_tensors(
+            [split.predictor_primary_key for split in splits]
+        )
+        merged.predictor_primary_value = cls._merge_list_tensors(
+            [split.predictor_primary_value for split in splits]
+        )
+        merged.predictor_importance_key = cls._merge_list_tensors(
+            [split.predictor_importance_key for split in splits]
+        )
+
+        return merged
+
+    def batch_repeat_interleave(self, repeats: int):
+        super().batch_repeat_interleave(repeats)
+        self.predictor_primary_key = self._repeat_list_tensors(
+            self.predictor_primary_key, repeats
+        )
+        self.predictor_primary_value = self._repeat_list_tensors(
+            self.predictor_primary_value, repeats
+        )
+        self.predictor_importance_key = self._repeat_list_tensors(
+            self.predictor_importance_key, repeats
+        )
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        super().batch_select_indices(indices)
+        self.predictor_primary_key = self._select_list_tensors(
+            self.predictor_primary_key, indices
+        )
+        self.predictor_primary_value = self._select_list_tensors(
+            self.predictor_primary_value, indices
+        )
+        self.predictor_importance_key = self._select_list_tensors(
+            self.predictor_importance_key, indices
+        )
+
+    @staticmethod
+    def _ensure_list_capacity(lst: list, idx: int, fill=None):
+        if len(lst) <= idx:
+            lst.extend([fill] * (idx + 1 - len(lst)))
+
+    @staticmethod
+    def _slice_list_tensors(
+        tensor_list: List[Optional[torch.Tensor]], start: int, end: int
+    ) -> List[Optional[torch.Tensor]]:
+        out = []
+        for t in tensor_list:
+            if t is None:
+                out.append(None)
+            else:
+                out.append(t[start:end, ...])
+        return out
+
+    @classmethod
+    def _merge_list_tensors(
+        cls, list_of_lists: List[List[Optional[torch.Tensor]]]
+    ) -> List[Optional[torch.Tensor]]:
+        # If no splits, return empty
+        if not list_of_lists:
+            return []
+
+        # Number of layers is length of the sub-list from the first split
+        max_len = len(list_of_lists[0])
+        merged = [None] * max_len
+
+        for layer_idx in range(max_len):
+            # collect that layer_idx from each split
+            chunk_tensors = []
+            for split in list_of_lists:
+                t = split[layer_idx] if layer_idx < len(split) else None
+                if t is not None:
+                    chunk_tensors.append(t)
+            if len(chunk_tensors) == 0:
+                merged[layer_idx] = None
+            else:
+                merged[layer_idx] = torch.cat(chunk_tensors, dim=0)
+        return merged
+
+    @staticmethod
+    def _repeat_list_tensors(
+        tensor_list: List[Optional[torch.Tensor]], repeats: int
+    ) -> List[Optional[torch.Tensor]]:
+        out = []
+        for t in tensor_list:
+            if t is None:
+                out.append(None)
+            else:
+                out.append(t.repeat_interleave(repeats, dim=0))
+        return out
+
+    @staticmethod
+    def _select_list_tensors(
+        tensor_list: List[Optional[torch.Tensor]], indices: torch.Tensor
+    ) -> List[Optional[torch.Tensor]]:
+        out = []
+        for t in tensor_list:
+            if t is None:
+                out.append(None)
+            else:
+                out.append(t.index_select(0, indices))
+        return out
+
+
+class TokenImportancePredictorAttentive(nn.Module):
+    def __init__(self, config, pred_hid_size, num_heads, num_hidden_layers, dDash, intdim, \
+                 attn_reduce_factor, dropout=0.1):
+        """
+        Optimized Token Importance Predictor with parallel Q-K projections and simplified mapping.
+        
+        Args:
+            config: Configuration object containing model parameters.
+            pred_hid_size (int): Hidden size for the predictor's attention layer.
+            num_heads (int): Number of attention heads.
+            num_hidden_layers (int): Number of transformer layers to predict.
+            dropout (float): Dropout probability.
+            q_downscale (int): Factor to downscale the Q dimension for efficiency.
+            intermediate_dim (int): Intermediate dimension for non-linear transformations in projections.
+        """
+        super().__init__()
+        self.config = config
+        self.hidden_size = pred_hid_size
+        self.num_heads = num_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.dropout = dropout
+        self.head_dim = pred_hid_size // (num_heads * 4) # Predictor head dimension is not the same as the model head dimension.
+        self.rope_theta = config.rope_theta
+        self.dDash = dDash
+        self.intermediate_dim = intdim
+        self.attn_reduce_factor = attn_reduce_factor
+        self.max_position_embeddings = config.max_position_embeddings
+        self.flash_attn = False
+        assert pred_hid_size % (num_heads * 4) == 0, "pred_hid_size must be divisible by num_heads * 4."
+
+        # Reduce the hidden size for attention computations
+        self.hidden_size_reduced = self.hidden_size // self.attn_reduce_factor  # For example, reduce to 1/4th
+        assert self.hidden_size_reduced % self.num_heads == 0, "Reduced hidden size must be divisible by num_heads"
+        self.attn_head_dim = self.hidden_size_reduced // self.num_heads
+
+        # Input projection to reduce hidden size
+        self.input_proj = nn.Linear(self.hidden_size, self.hidden_size_reduced, bias=False)
+
+        # Query, Key, Value projections for attention
+        self.q_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        self.k_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        self.v_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        # Output projection to restore hidden size
+        # self.o_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        self.attn_dropout = nn.Dropout(self.dropout)
+
+        # LayerNorm and Feed-forward network
+        self.norm1 = nn.LayerNorm(self.hidden_size_reduced)
+        self.norm2 = nn.LayerNorm(self.hidden_size)
+
+        self.ffn_hidden_size = 2 * self.hidden_size_reduced  # Typical FFN hidden size
+        self.ffn = nn.Sequential(
+            nn.Linear(self.hidden_size_reduced, self.ffn_hidden_size),
+            nn.GELU(),
+            nn.Linear(self.ffn_hidden_size, self.hidden_size),
+            nn.Dropout(self.dropout)
+        )
+        # Add extra LayerNorm for the importance branch when not using the old design.
+        self.norm_importance = nn.LayerNorm(self.hidden_size)
+
+        # Define Q and K projection layers for all layers in parallel with non-linearity[]
+        # Output shape: [B, L, N * H * D']
+        self.q_proj_importance = nn.Sequential(
+            nn.Linear(pred_hid_size, self.intermediate_dim, bias=False),
+            nn.SiLU(),
+            nn.Linear(self.intermediate_dim, num_hidden_layers * num_heads * self.dDash, bias=False)
+        )
+        self.k_proj_importance = nn.Sequential(
+            nn.Linear(pred_hid_size, self.intermediate_dim, bias=False),
+            nn.SiLU(),
+            nn.Linear(self.intermediate_dim, num_hidden_layers * num_heads * self.dDash, bias=False)
+        )
+
+        # Initialize rotary positional embeddings
+        self._init_rope()
+        self._initialize_weights()
+        self.device = None
+
+    def _initialize_weights(self):
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight)  # Xavier initialization for linear layers
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+            elif isinstance(module, nn.LayerNorm):
+                nn.init.constant_(module.weight, 1.0)
+                nn.init.constant_(module.bias, 0.0)
+            elif isinstance(module, nn.MultiheadAttention):
+                # Initialize in_proj_weight
+                nn.init.xavier_uniform_(module.in_proj_weight)
+                if module.in_proj_bias is not None:
+                    nn.init.constant_(module.in_proj_bias, 0)
+
+                # Initialize out_proj
+                nn.init.xavier_uniform_(module.out_proj.weight)
+                if module.out_proj.bias is not None:
+                    nn.init.constant_(module.out_proj.bias, 0)
+
+    def _init_rope(self):
+
+        # send self.config but after modifying head_dim to be self.head_dim just in the function call
+        config_copy = copy.deepcopy(self.config)
+        config_copy.rope_scaling = {
+            "factor": 32.0,
+            "high_freq_factor": 4.0,
+            "low_freq_factor": 1.0,
+            "original_max_position_embeddings": 8192,
+            "rope_type": "llama3"
+        }
+        config_copy.head_dim = self.attn_head_dim
+        
+        # Rotary embedding for attention layer
+        self.rotary_emb_attn = LlamaRotaryEmbedding(
+            config_copy
+        )
+
+        config_copy.head_dim = self.dDash
+        # Rotary embedding for importance projection
+        self.rotary_emb_importance = LlamaRotaryEmbedding(
+            config_copy
+        )
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, use_cache=False, layer_idx=None):
+        """
+        Forward pass for the Optimized Token Importance Predictor.
+        
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape [B, L, HQ].
+            attention_mask (torch.Tensor, optional): Attention mask of shape [B, 1, 1, L] or [B, 1, L, L].
+            position_ids (torch.Tensor, optional): Position IDs.
+            past_key_value (tuple, optional): Past key and value states.
+            use_cache (bool, optional): Whether to use cache.
+        
+        Returns:
+            torch.Tensor: Importance scores of shape [B, N, H, L, L].
+        """
+        layer_idx = 0 # Guaranteed to be 0, as we only have one predictor!
+
+        # Set device if not already set
+        if self.device != hidden_states.device:
+            self.device = hidden_states.device
+            self.to(self.device)
+            
+        B, L, E = hidden_states.size()
+        
+        # Reduce hidden size
+        hidden_states = hidden_states.to(self.input_proj.weight.dtype)
+        hidden_states_reduced = self.input_proj(hidden_states)  # [B, L, hidden_size_reduced]
+        # Compute q, k, v for attention
+        q = self.q_proj_attn(hidden_states_reduced)  # [B, L, hidden_size_reduced]
+        k = self.k_proj_attn(hidden_states_reduced)  # [B, L, hidden_size_reduced]
+        v = self.v_proj_attn(hidden_states_reduced)  # [B, L, hidden_size_reduced]
+        # Reshape q, k, v to [B, num_heads, L, attn_head_dim]
+        q = q.view(B, L, self.num_heads, self.attn_head_dim).transpose(1, 2)  # [B, num_heads, L, attn_head_dim]
+        k = k.view(B, L, self.num_heads, self.attn_head_dim).transpose(1, 2)  # [B, num_heads, L, attn_head_dim]
+        v = v.view(B, L, self.num_heads, self.attn_head_dim).transpose(1, 2)  # [B, num_heads, L, attn_head_dim]
+        if (past_key_value is not None
+            and layer_idx < len(past_key_value.predictor_primary_key)
+            and past_key_value.predictor_primary_key[layer_idx] is not None):
+            offset = past_key_value.predictor_primary_key[layer_idx].shape[2]  # old_k.shape[2]
+        else:
+            offset = 0
+
+        # total seq length for new + old
+        kv_seq_len = offset + L
+
+        # Step 2: build position_ids for just the new chunk [offset..offset+L-1]
+        if position_ids is None:
+            # shape [B, L], e.g. [0..(offset+L-1)]
+            position_ids = torch.arange(offset, offset + L, dtype=torch.long, device=self.device)
+            position_ids = position_ids.unsqueeze(0).expand(B, L)
+
+        # Step 3: apply rotary to just the new chunk k,v with the correct offset
+        cos, sin = self.rotary_emb_attn(v, position_ids)
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
+
+        # Step 4: ask the cache to append them.  Then re‐assign k, v to the full cat
+        if use_cache and past_key_value is not None:
+            k, v = past_key_value.update_predictor_primary(k.detach(), v.detach(), layer_idx)
+            kv_seq_len = k.size(2)  # now includes old + new
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True)
+        attn_output = attn_output.to(q.dtype)
+        attn_output = attn_output.transpose(1, 2).contiguous().view(B, L, self.hidden_size_reduced)
+        attn_output = self.norm1(attn_output)
+        ffn_output = self.ffn(attn_output)
+        # Temporary measure, till old predictor fully deprecated
+        hidden_states = self.norm2(hidden_states + ffn_output)
+
+        B, L, E = hidden_states.size()
+        # Importance projections
+        H = self.num_heads
+        N = self.num_hidden_layers
+
+        hidden_states_for_importance = self.norm_importance(hidden_states)
+        q_importance = self.q_proj_importance(hidden_states_for_importance)
+        k_importance = self.k_proj_importance(hidden_states_for_importance)
+
+        # Reshape and permute to [B, N, H, L, D']
+        q_importance = q_importance.view(B, L, N, H, self.dDash).permute(0, 2, 3, 1, 4).contiguous()  # [B, N, H, L, D']
+        k_importance = k_importance.view(B, L, N, H, self.dDash).permute(0, 2, 3, 1, 4).contiguous()  # [B, N, H, L, D']
+
+        # Flatten N and H for efficient computation
+        q_importance = q_importance.view(B * N * H, L, self.dDash)  # [BNH, L, D']
+        k_importance = k_importance.view(B * N * H, L, self.dDash)  # [BNH, L, D']
+
+        # Apply rotary positional embeddings
+        cos, sin = self.rotary_emb_importance(k_importance, position_ids)
+        q_importance, k_importance = apply_rotary_pos_emb(q_importance, k_importance, cos, sin, position_ids)
+
+        if use_cache and past_key_value is not None:
+            k_importance = past_key_value.update_predictor_importance(k_importance.detach(), layer_idx)
+            
+        k_importance = k_importance.view(B * H, N, -1, self.dDash)  # [BNH, L, D']
+        q_importance = q_importance.view(B * H, N, -1, self.dDash)  # [BH, N, L, D']
+        return q_importance, k_importance
+
+
+
+class HeadImportancePredictor(nn.Module):
+    def __init__(self, config, pred_hid_size, num_heads, num_hidden_layers, dDash, intdim, \
+                 attn_reduce_factor, dropout=0.1):
+        """
+        Optimized Token Importance Predictor with parallel Q-K projections and simplified mapping.
+        
+        Args:
+            config: Configuration object containing model parameters.
+            pred_hid_size (int): Hidden size for the predictor's attention layer.
+            num_heads (int): Number of attention heads.
+            num_hidden_layers (int): Number of transformer layers to predict.
+            dropout (float): Dropout probability.
+            q_downscale (int): Factor to downscale the Q dimension for efficiency.
+            intermediate_dim (int): Intermediate dimension for non-linear transformations in projections.
+        """
+        super().__init__()
+        self.is_head_predictor = None
+        self.config = config
+        self.hidden_size = pred_hid_size
+        self.num_heads = num_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.dropout = dropout
+        self.head_dim = pred_hid_size // (num_heads * 4)
+        self.rope_theta = config.rope_theta
+        self.dDash = dDash
+        self.intermediate_dim = intdim
+        self.attn_reduce_factor = attn_reduce_factor
+        self.max_position_embeddings = config.max_position_embeddings
+        self.flash_attn = False
+
+        # Reduce the hidden size for attention computations
+        self.hidden_size_reduced = self.hidden_size // self.attn_reduce_factor  # For example, reduce to 1/4th
+        assert self.hidden_size_reduced % self.num_heads == 0, "Reduced hidden size must be divisible by num_heads"
+        self.attn_head_dim = self.hidden_size_reduced // self.num_heads
+
+        # Input projection to reduce hidden size
+        self.input_proj = nn.Linear(self.hidden_size, self.hidden_size_reduced, bias=False)
+
+        # Query, Key, Value projections for attention
+        self.q_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        self.k_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        self.v_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        # Output projection to restore hidden size
+        # self.o_proj_attn = nn.Linear(self.hidden_size_reduced, self.hidden_size_reduced, bias=False)
+        self.attn_dropout = nn.Dropout(self.dropout)
+
+        # LayerNorm and Feed-forward network
+        self.norm1 = nn.LayerNorm(self.hidden_size_reduced)
+        self.norm2 = nn.LayerNorm(self.hidden_size)
+
+        self.ffn_hidden_size = 4 * self.hidden_size_reduced  # Typical FFN hidden size
+        self.ffn = nn.Sequential(
+            nn.Linear(self.hidden_size_reduced, self.ffn_hidden_size),
+            nn.GELU(),
+            nn.Linear(self.ffn_hidden_size, self.num_heads * self.num_hidden_layers),
+        )
+
+        # Initialize rotary positional embeddings
+        self._init_rope()
+        self._initialize_weights()
+        self.device = None
+
+    def _initialize_weights(self):
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                nn.init.xavier_uniform_(module.weight)  # Xavier initialization for linear layers
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+            elif isinstance(module, nn.LayerNorm):
+                nn.init.constant_(module.weight, 1.0)
+                nn.init.constant_(module.bias, 0.0)
+            elif isinstance(module, nn.MultiheadAttention):
+                # Initialize in_proj_weight
+                nn.init.xavier_uniform_(module.in_proj_weight)
+                if module.in_proj_bias is not None:
+                    nn.init.constant_(module.in_proj_bias, 0)
+
+                # Initialize out_proj
+                nn.init.xavier_uniform_(module.out_proj.weight)
+                if module.out_proj.bias is not None:
+                    nn.init.constant_(module.out_proj.bias, 0)
+
+    def _init_rope(self):
+        config_copy = copy.deepcopy(self.config)
+        config_copy.head_dim = self.attn_head_dim
+        # Rotary embedding for attention layer
+        self.rotary_emb_attn = LlamaRotaryEmbedding(
+            config_copy
+        )
+        # Rotary embedding for importance projection
+        self.rotary_emb_importance = LlamaRotaryEmbedding(
+            config_copy
+        )
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, use_cache=False):
+        """
+        Forward pass for the Optimized Token Importance Predictor.
+        
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape [B, L, HQ].
+            attention_mask (torch.Tensor, optional): Attention mask of shape [B, 1, 1, L] or [B, 1, L, L].
+            position_ids (torch.Tensor, optional): Position IDs.
+            past_key_value (tuple, optional): Past key and value states.
+            use_cache (bool, optional): Whether to use cache.
+        
+        Returns:
+            torch.Tensor: Importance scores of shape [B, N, H, L, L].
+        """
+        # Set device if not already set
+        if self.device != hidden_states.device:
+            self.device = hidden_states.device
+            self.to(self.device)
+
+        B, L, E = hidden_states.size()
+        if past_key_value is None:
+            past_key_value = {}
+        # if L == 1:
+        #     import pdb; pdb.set_trace()
+        past_primary = past_key_value.get('primary', None)
+        # Reduce hidden size
+        hidden_states = hidden_states.to(self.input_proj.weight.dtype)
+        hidden_states_reduced = self.input_proj(hidden_states)  # [B, L, hidden_size_reduced]
+        # Compute q, k, v for attention
+        q = self.q_proj_attn(hidden_states_reduced)  # [B, L, hidden_size_reduced]
+        k = self.k_proj_attn(hidden_states_reduced)  # [B, L, hidden_size_reduced]
+        v = self.v_proj_attn(hidden_states_reduced)  # [B, L, hidden_size_reduced]
+        # Reshape q, k, v to [B, num_heads, L, attn_head_dim]
+        q = q.view(B, L, self.num_heads, self.attn_head_dim).transpose(1, 2)  # [B, num_heads, L, attn_head_dim]
+        k = k.view(B, L, self.num_heads, self.attn_head_dim).transpose(1, 2)  # [B, num_heads, L, attn_head_dim]
+        v = v.view(B, L, self.num_heads, self.attn_head_dim).transpose(1, 2)  # [B, num_heads, L, attn_head_dim]
+        # Compute kv_seq_len before concatenation
+        if past_primary is not None:
+            past_L = past_primary[0].shape[2]
+            kv_seq_len = past_L + L
+        else:
+            kv_seq_len = L
+        
+        # Apply rotary positional embeddings based on kv_seq_len
+        cos, sin = self.rotary_emb_attn(v, position_ids)
+        if position_ids is None:
+            position_ids = torch.arange(kv_seq_len, dtype=torch.long, device=self.device)
+            position_ids = position_ids.unsqueeze(0).expand(B, kv_seq_len)
+        
+        if past_primary is not None:
+            # Concatenate past k and v
+            k = torch.cat([past_primary[0], k], dim=2)  # [B, num_heads, past_L + L, attn_head_dim]
+            v = torch.cat([past_primary[1], v], dim=2)  # [B, num_heads, past_L + L, attn_head_dim]
+        
+        # Apply rotary embeddings after concatenation
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
+        
+        # Update cache if use_cache is True
+        if use_cache:
+            past_key_value['primary'] = (k.detach(), v.detach())
+
+        # if self.flash_attn:
+        #     sm_scale = 1.0 / math.sqrt(self.attn_head_dim)
+        #     attn_output = attention(q.contiguous().to(torch.float16), k.contiguous().to(torch.float16), v.contiguous().to(torch.float16), True, sm_scale).to(q.dtype)
+        # else:
+        #     attn_output = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True)
+        attn_output = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True)
+        attn_output = attn_output.to(q.dtype)
+        attn_output = attn_output.transpose(1, 2).contiguous().view(B, L, self.hidden_size_reduced)
+        attn_output = self.norm1(attn_output)
+        head_importances = self.ffn(attn_output)
+        return head_importances, past_key_value
+
+def calculate_hit_metrics(estimated_importance: torch.Tensor, 
+                          true_importance: torch.Tensor, 
+                          top_k_ratio: float = 0.5) -> Tuple[float, float, float]:
+    """
+    Calculate hit accuracy, mean, and max rank correlation between estimated and true importance tensors.
+    We compute metrics along the last dimension of the input tensors.
+
+    Shapes:
+      - 4D token-importance: [B, H, L, L]. We slice the last query (index -1) => [B, H, L].
+      - 3D head-importance:  [B, L, H]. We use all of it as-is => [B, L, H].
+    
+    Args:
+        estimated_importance (torch.Tensor): [B, H, L, L] or [B, L, H]
+        true_importance      (torch.Tensor): [B, H, L, L] or [B, L, H]
+        top_k_ratio (float): Fraction of top-k elements to consider for hit accuracy (default=0.5).
+    
+    Returns:
+        (hit_accuracy, mean_corr, max_corr):
+            hit_accuracy (float): Intersection ratio of top-k sets (0..1).
+            mean_corr (float): Average Spearman rank correlation over all [B, ...].
+            max_corr (float): Maximum Spearman rank correlation among all [B, ...].
+    """
+
+    # 1) Standardize shapes so the last dimension is what we rank over.
+    if estimated_importance.dim() == 4:
+        # Shape is [B, H, L, L] => slice to keep only the last query => [B, H, L]
+        estimated_importance = estimated_importance[:, :, -1, :]
+        true_importance      = true_importance[:, :, -1, :]
+        # after slicing: [B, H, L]
+        # For intersection denominator => top_k * B * H
+        denom_for_hits = estimated_importance.size(0) * estimated_importance.size(1)
+    elif estimated_importance.dim() == 3:
+        # Shape is [B, L, H], the last dimension is H
+        # For intersection denominator => top_k * B * L
+        denom_for_hits = estimated_importance.size(0) * estimated_importance.size(1)
+    else:
+        raise ValueError("Tensors must be either 4D [B,H,L,L] or 3D [B,L,H].")
+
+    # 2) Compute Spearman rank correlation along the last dimension.
+    #    Sort indices in descending order => get 'ranks' for correlation.
+    _, sorted_esti = torch.sort(estimated_importance, dim=-1, descending=True)
+    _, sorted_true = torch.sort(true_importance, dim=-1, descending=True)
+
+    # Spearman's rho = 1 - 6 * sum(d^2) / [n*(n^2 - 1)]
+    n = sorted_esti.shape[-1]
+    d = sorted_esti.float() - sorted_true.float()
+    d_squared = d ** 2
+    sum_d_squared = d_squared.sum(dim=-1)
+    rank_corr = 1 - (6 * sum_d_squared) / (n * (n**2 - 1))  # shape: [B,H] or [B,L]
+
+    mean_corr = rank_corr.mean().item()
+    max_corr  = rank_corr.max().item()
+
+    # 3) Compute top-k hit accuracy along the last dimension.
+    top_k = max(1, int(n * top_k_ratio))
+    _, top_esti_indices = torch.topk(estimated_importance, top_k, dim=-1)
+    _, top_true_indices = torch.topk(true_importance,      top_k, dim=-1)
+
+    # top_esti_indices => [B,H,top_k] or [B,L,top_k]
+    # top_true_indices => [B,H,top_k] or [B,L,top_k]
+    # matches => [B,H,top_k,top_k] or [B,L,top_k,top_k]
+    matches = (top_esti_indices.unsqueeze(-1) == top_true_indices.unsqueeze(-2))
+    intersection = matches.any(dim=-1).sum(dim=-1)  # => [B,H] or [B,L]
+
+    # Each [B,H] or [B,L] element can have at most 'top_k' matches, so total is top_k * denom_for_hits.
+    total_possible = top_k * denom_for_hits
+    hit_accuracy = intersection.sum().item() / total_possible  # => 0..1
+
+    return hit_accuracy, mean_corr, max_corr
+
+
+def threshold_to_mask(unadj_importance_mask, perhead_thresholds, min_sparse_index, bsz, q_len, key_len):
+    """
+    Create a mask tensor based on per-head thresholds, setting values below the threshold to -inf.
+    
+    Args:
+    - unadj_importance_mask: torch.Tensor of shape [B, H, Lq, Lk].
+    - perhead_thresholds: torch.Tensor of shape [H], per-head thresholds.
+    - min_sparse_index: Minimum index for sparsity; values below this index will not be masked.
+    - bsz: Batch size.
+    - q_len: Query length (Lq).
+    - key_len: Key length (Lk).
+
+    Returns:
+    - mask_tensor: torch.Tensor of shape [B, H, Lq, Lk], with values below threshold as -inf.
+    """
+    # Ensure perhead_thresholds is in the correct shape for broadcasting
+    thresholds_broadcast = perhead_thresholds.view(1, -1, 1, 1)  # [1, H, 1, 1]
+
+    # Compare unadj_importance_mask with thresholds to create a mask
+    mask_tensor = torch.where(
+        unadj_importance_mask >= thresholds_broadcast, 
+        torch.zeros_like(unadj_importance_mask), 
+        torch.full_like(unadj_importance_mask, float('-inf'))
+    )  # [B, H, Lq, Lk]
+
+    # Ensure mask_tensor has mask_tensor[:, :, :, :min_sparse_index] = 0
+    mask_tensor[:, :, :, :min_sparse_index] = 0.0
+
+    return mask_tensor
+
+class SlidingWindowCache:
+    def __init__(self, max_seq_len, sliding_window, device):
+        self.sliding_window = sliding_window
+        self.device = device
+        if sliding_window is None:
+            self.max_seq_len = 0
+            self.window = None
+        else:
+            self.max_seq_len = max_seq_len
+            self.window = self._create_window(self.max_seq_len)
+
+    def _create_window(self, seq_len):
+        idx = torch.arange(seq_len, device=self.device)
+        query = idx.unsqueeze(1)  # [seq_len, 1]
+        key = idx.unsqueeze(0)    # [1, seq_len]
+        win = (key >= (query - self.sliding_window + 1)) & (key <= query)
+        return win.unsqueeze(0).unsqueeze(0)  # [1,1,seq_len,seq_len]
+
+    def get_window(self, q_len, key_len):
+        if self.sliding_window is None:
+            return None
+        req = max(q_len, key_len)
+        if req > self.max_seq_len:
+            self.max_seq_len = req
+            self.window = self._create_window(self.max_seq_len)
+        return self.window[:, :, :q_len, :key_len]
+
+def enforce_sliding_window(mask_tensor, window):
+    if window is None:
+        return mask_tensor
+    return mask_tensor.masked_fill(window, 0.0)
+
+
+def sorted_index_to_mask(
+    sorted_indices,
+    attention_mask,
+    min_sparse_index,
+    bsz,
+    q_len,
+    key_len,
+    sparse_aggression,
+    sliding_window=None
+):
+    """
+    sorted_indices: [B, H, q_len, key_len]
+    attention_mask: [1, 1, q_len, key_len]  (True = keep, False = mask out, or vice versa)
+    min_sparse_index: guaranteed front region to keep
+    sliding_window: guaranteed trailing region (for each query) to keep
+    sparse_aggression: float in [0,1], fraction of keys to drop or keep
+    """
+    device = sorted_indices.device
+    dtype = sorted_indices.dtype
+
+    # Step 1: Compute base K
+    if q_len == 1:  
+        query_positions = torch.arange(q_len, device=device).view(1, 1, q_len, 1).float()
+        query_positions[0] = key_len + 1
+    else:
+        query_positions = torch.arange(q_len, device=device).view(1, 1, q_len, 1).float() + 1.0
+    K_original = torch.ceil(query_positions * sparse_aggression).long()  # [1,1,q_len,1]
+    K_original = torch.clamp(K_original, max=key_len)
+
+    # Step 1b: Incorporate guaranteed region
+    guaranteed = min_sparse_index
+    if sliding_window is not None:
+        guaranteed += sliding_window
+    # Subtract guaranteed from the original K
+    K_adjusted = K_original - guaranteed
+    # Ensure K_adjusted is at least 0
+    K_adjusted = torch.clamp(K_adjusted, min=0, max=key_len)
+
+    # Step 2: Expand attention_mask to [B,H,q_len,key_len]
+    attention_mask_expanded = attention_mask.expand(bsz, -1, -1, -1)
+    attention_mask_expanded = attention_mask_expanded.expand(-1, sorted_indices.size(1), -1, -1)
+    # Convert True -> 1, False -> 0
+    attention_mask_expanded = (~attention_mask_expanded.bool()).int()
+
+    # Step 3: Gather (reorder) mask by sorted_indices
+    gathered_mask = torch.gather(attention_mask_expanded, dim=-1, index=sorted_indices)
+
+    # Step 4: cumsum along sorted dimension
+    gathered_mask_float = gathered_mask.float()
+    cum_sum = torch.cumsum(gathered_mask_float, dim=-1)  # [B,H,q_len,key_len]
+
+    # Step 5: Compare cumsum <= K_adjusted
+    # Expand K_adjusted to [B,H,q_len,key_len] for broadcast
+    K_broadcast = K_adjusted.view(1, 1, q_len, 1).expand_as(cum_sum)
+    selected_mask = (cum_sum <= K_broadcast)
+
+    # Step 6: Prepare final mask_tensor with -inf by default
+    mask_tensor = torch.full_like(attention_mask_expanded.float(), float('-inf'))
+
+    # Step 7: Scatter 0 where selected, -inf otherwise
+    scatter_values = torch.zeros_like(gathered_mask_float)
+    scatter_values = scatter_values.masked_fill(~selected_mask, float('-inf'))
+    mask_tensor.scatter_(-1, sorted_indices, scatter_values)
+
+    # Step 8: Force the guaranteed front region unmasked
+    mask_tensor[:, :, :, :min_sparse_index] = 0.0
+
+    # We do NOT forcibly unmask the trailing `sliding_window` here,
+    # because we typically do it with a separate function that
+    # ensures the last `sliding_window` positions are unmasked for each query.
+    # Replace with self.sliding_window where referenced
+    # Where not referenced, reduce budget in calculation.
+
+    return mask_tensor
+
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, config=None):
+        self.scaling_factor = scaling_factor
+        super().__init__(config)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, config=None):
+        self.scaling_factor = scaling_factor
+        super().__init__(config)
+
+    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.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class LlamaAttentionExperimental(nn.Module):
+    def __init__(self, config: LlamaConfig, producer=None, layer_idx=0):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_hidden_layers = config.num_hidden_layers
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.inference_mode = False
+        self.producer = producer
+        self.layer_idx = layer_idx
+        self.token_sparse_method = None
+        self.sparse_aggression = None
+        self.stream_llm_start_size = None
+        self.dDash = None
+        self.intdim = None
+        self.attn_reduce_factor = None
+        self.head_attn_reduce_factor = None
+        self.effective_sparsity = None
+        self.min_sparse_index = None
+        self.pred_hid_size = self.hidden_size
+        self.num_tok_per_page = None
+        self.calc_hitrates = False
+        self.flash_attn = False
+        self.train_headpredictor = False
+        self.calibrate_thresholds = False
+        self.test_with_thresholds = False
+        self.old_predictor = None
+
+        if self.layer_idx > 0:
+            self.mseloss = MSELoss(reduction='none')
+            self.msemagn_loss = None
+            self.headmseloss = MSELoss(reduction='none')
+            self.headmsemagn_loss = None
+        
+        if self.producer is None:  # This is the producer layer
+            self.q_importance = None  # Shared mask across layers during inference
+            self.k_importance = None
+            self.head_importances = None
+            self.actmagn_masklist = {}
+            self.available_tokens = {}
+
+        # Attention setup
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        self._init_rope()
+        
+    def update_predictor(self):
+        self.sparse_token_predictor = TokenImportancePredictorAttentive(
+            self.config, self.pred_hid_size, self.num_heads, self.num_layers_pred, dropout=0.1, dDash = self.dDash, \
+            intdim = self.intdim, attn_reduce_factor=self.attn_reduce_factor
+        ).to('cuda:0')
+        self.sparse_token_predictor.flash_attn = self.flash_attn
+        if self.train_headpredictor:
+            self.sparse_head_predictor = HeadImportancePredictor(
+                self.config, self.pred_hid_size, self.num_heads, self.num_layers_pred, dropout=0.1, dDash = self.dDash, \
+                intdim = self.intdim, attn_reduce_factor=self.head_attn_reduce_factor
+            ).to('cuda:0')
+            self.sparse_head_predictor.flash_attn = self.flash_attn
+
+    def set_token_sparsity(self):
+        assert self.token_sparse_method is not None, "Set token sparse method first!"
+        if self.token_sparse_method is not None:
+            try:
+                mname = self.config._name_or_path.split("/")[-1]
+                read_path = f"threshold_calibs/{mname}/{self.token_sparse_method}.pkl"
+                threshold_model_dictionary = torch.load(read_path)
+                self.tok_calibration_set = threshold_model_dictionary
+            except:
+                pass
+        if self.token_sparse_method == "LazyLLM":
+            if self.layer_idx <= 9:
+                self.sparse_aggression = 1
+            elif self.layer_idx <= 19:
+                self.sparse_aggression = 0.7
+            elif self.layer_idx <= 28:
+                self.sparse_aggression = 0.4
+            else:
+                self.sparse_aggression = 0.1
+        elif "fixed" in self.token_sparse_method:
+            if self.layer_idx == 0:
+                self.sparse_aggression = 1
+            else:
+                self.sparse_aggression = 1 - float(self.token_sparse_method.split("_")[1].split("pc")[0])/100.
+        elif "progressive" in self.token_sparse_method:
+            pc_drop = float(self.token_sparse_method.split("_")[1].split("pc")[0])/100.
+            self.sparse_aggression = (1 - pc_drop) ** (self.layer_idx)  # (x% per layer, progressive_xpc style)
+        else:
+            raise ValueError(f"Unknown token sparsity method {self.token_sparse_method}")
+            
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(
+                self.config
+            )
+        else:
+            scaling_type = self.config.rope_scaling.get("type") or self.config.rope_scaling.get("rope_type")
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear" or scaling_type == 'llama3':
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                    config=self.config
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                    config=self.config
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Union[DynamicCache, PredictorDynamicCache]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        padding_mask: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[PredictorDynamicCache]]:
+        bsz, q_len, _ = hidden_states.size()
+        Ltrack = hidden_states.size(1)
+
+        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)
+
+        evalmode = self.eval_llm_mode
+        num_tokens_to_keep = int(q_len * self.sparse_aggression)
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) # AHMED: Modified this to use the newer version.
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        
+        if use_cache:
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
+
+        kv_seq_len = key_states.shape[-2]
+        final_mask = None
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        key_len = key_states.size(2)
+        bsz, q_len = query_states.size(0), query_states.size(2)
+
+        if attention_mask is None:
+            # We want a [q_len, kv_seq_len] boolean upper-triangular mask
+            causal_mask_2d = torch.ones(q_len, kv_seq_len, 
+                                        device=hidden_states.device, 
+                                        dtype=torch.bool).triu(diagonal=1)
+            # Then shape it to [bsz, 1, q_len, kv_seq_len]
+            causal_mask_4d = causal_mask_2d.unsqueeze(0).expand(bsz, 1, q_len, kv_seq_len)
+            # Now fill -inf where the mask is True
+            attention_mask = torch.full_like(causal_mask_4d, 0, dtype=hidden_states.dtype)
+            if q_len != 1:
+                attention_mask = attention_mask.masked_fill(causal_mask_4d, float("-inf"))
+
+        if self.inference_mode:
+            min_sparse_index = self.min_sparse_index
+            with torch.no_grad():
+                if evalmode == "ExpPred":
+                    if self.layer_idx > 0:
+                        q_importance_tensor = self.producer.q_importance[:, self.layer_idx % self.producer_frequency, :, :].float().to(query_states.device) # [BH, Lq, D']
+                        k_importance_tensor = self.producer.k_importance[:, self.layer_idx % self.producer_frequency, :, :].float().to(key_states.device) # [BH, Lk, D']
+                        importance_mask = torch.bmm(q_importance_tensor, k_importance_tensor.transpose(-2, -1)) / math.sqrt(self.dDash) # [BH, Lq, Lk]
+                        importance_mask = importance_mask.view(bsz, self.num_heads, q_len, key_len) # [B, H, Lq, Lk]
+                        attn_weights = torch.matmul(query_states, key_states.transpose(-2, -1)) / math.sqrt(self.head_dim)
+                        if self.calc_hitrates:
+                            self.tok_hit_acc, self.tok_mean_rank_corr, self.tok_max_rank_corr = calculate_hit_metrics(
+                                estimated_importance=importance_mask,
+                                true_importance=attn_weights,
+                                top_k_ratio=0.5
+                            )
+                        if self.calibrate_thresholds:
+                            ### Threshold variance investigation
+                            unadj_importance_mask = importance_mask.clone()
+                            importance_mask = torch.softmax(importance_mask + attention_mask, dim=-1)
+                            sorted_indices = torch.argsort(importance_mask, dim=-1, descending=True)
+                            sorted_indices = sorted_indices[:, :, -q_len:, :]
+                            sorted_values, sorted_ix = torch.sort(importance_mask, dim=-1)
+                            sorted_true_values, _ = torch.sort(torch.gather(unadj_importance_mask, dim=-1, index=sorted_ix), dim=-1)
+                            true_thresholds = sorted_true_values[:, :, :, int(importance_mask.size(-1) * self.sparse_aggression)]
+                            thresholds = sorted_values[:, :, :, int(importance_mask.size(-1) * self.sparse_aggression)]
+                            self.true_threshmean = true_thresholds
+                            self.threshmean = thresholds
+                        if self.test_with_thresholds:
+                            unadj_importance_mask = importance_mask.clone()
+                            perhead_thresholds = self.tok_calibration_set[self.layer_idx - 1].to(unadj_importance_mask.device) # 0 does not have calibration data.
+                            mask_tensor = threshold_to_mask(unadj_importance_mask, perhead_thresholds, min_sparse_index, bsz, q_len, key_len)
+                        else:
+                            importance_mask = torch.softmax(importance_mask + attention_mask, dim=-1)
+                            sorted_indices = torch.argsort(importance_mask, dim=-1, descending=True)
+                            sorted_indices = sorted_indices[:, :, -q_len:, :]
+                            mask_tensor = sorted_index_to_mask(sorted_indices, attention_mask, min_sparse_index, bsz, q_len, key_len, self.sparse_aggression, self.sliding_window)
+                        ### Threshold variance investigation
+                        if self.sliding_window is not None:
+                            if not hasattr(self, "window_cache"):
+                                self.window_cache = SlidingWindowCache(max_seq_len=1024,
+                                                                    sliding_window=self.sliding_window,
+                                                                    device=mask_tensor.device)
+                            window = self.window_cache.get_window(q_len, key_len)
+                            mask_tensor = enforce_sliding_window(mask_tensor, window)
+                        final_mask = mask_tensor
+
+                        self.final_mask_investigate = final_mask
+                        attn_weights = attn_weights + mask_tensor + attention_mask
+                    else:
+                        attn_weights = torch.matmul(query_states, key_states.transpose(-2, -1)) / math.sqrt(self.head_dim)
+                        attn_weights = attn_weights + attention_mask
+                else:
+                    raise ValueError(f"Unknown eval mode {evalmode}")
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+        else:
+            attn_weights = torch.matmul(query_states, key_states.transpose(-2, -1)) / math.sqrt(self.head_dim)   
+            if self.layer_idx > 0:
+                q_importance_tensor = self.producer.q_importance[:, self.layer_idx % self.producer_frequency, :, :].float().to(query_states.device) # [BH, Lq, D']
+                k_importance_tensor = self.producer.k_importance[:, self.layer_idx % self.producer_frequency, :, :].float().to(key_states.device) # [BH, Lk, D']
+                importance_mask = torch.bmm(q_importance_tensor, k_importance_tensor.transpose(-2, -1)) / math.sqrt(self.dDash) # [BH, Lq, Lk]
+                importance_mask = importance_mask.view(bsz, self.num_heads, q_len, key_len) # [B, H, Lq, Lk]
+
+                if self.lookahead == 0:
+                    self.msemagn_loss = self.mseloss(attn_weights, importance_mask)
+                else:
+                    self.msemagn_loss = self.mseloss(attn_weights[:, :, self.lookahead:, :], importance_mask[:, :, :-self.lookahead, :])
+                self.msemagn_loss = (self.msemagn_loss).mean(dim=(-1, -2))
+                self.msemagn_loss = self.msemagn_loss.mean()
+
+                if self.calc_hitrates:
+                    self.tok_hit_acc, self.tok_mean_rank_corr, self.tok_max_rank_corr = calculate_hit_metrics(
+                        estimated_importance=importance_mask,
+                        true_importance=attn_weights,
+                        top_k_ratio=0.5
+                    )
+
+            if attention_mask is not None:
+                attn_weights = attn_weights + attention_mask
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+        if self.layer_idx > 0 and self.train_headpredictor:
+            head_importance_tensor = self.producer.head_importances[:, :, :, self.layer_idx % self.producer_frequency].float().to(attn_output.device)
+            attn_head_weights = attn_output.mean(dim=-1).permute(0, 2, 1)
+            self.headmsemagn_loss = self.headmseloss(attn_head_weights, head_importance_tensor).mean()
+
+            if self.calc_hitrates:
+                self.head_hit_acc, self.head_mean_rank_corr, self.head_max_rank_corr = calculate_hit_metrics(
+                    estimated_importance=head_importance_tensor,
+                    true_importance=attn_head_weights,
+                    top_k_ratio=0.5
+                )
+        else:
+            self.headmsemagn_loss = 0
+            if self.calc_hitrates:
+                self.head_hit_acc, self.head_mean_rank_corr, self.head_max_rank_corr = 0, 0, 0
+
+            
+        checkeverytime = hasattr(self, 'test_with_thresholds')
+        if checkeverytime:
+            checkeverytime = self.test_with_thresholds
+        if final_mask is not None:
+            if self.effective_sparsity is None or checkeverytime:
+                true_mask = final_mask + attention_mask
+                num_deact = true_mask.bool().sum(dim=-1)                   # Number of tokens disabled.
+                causally_deact = (attention_mask.bool()).sum(dim=-1).expand_as(num_deact)        # Number of tokens disabled causally anyway
+                additional_deact = (num_deact - causally_deact)
+                num_active = (~attention_mask.bool()).sum(dim=-1).expand_as(num_deact)    # Number of tokens active at this position if zero-sparsity
+                effective_sparsity = 100 * (additional_deact.float() / num_active.float()).mean().item()
+                self.effective_sparsity = effective_sparsity
+                print("Effective Sparsity:", effective_sparsity, "%\t Sequence Length:", q_len)
+        if self.layer_idx == 0:
+            if self.effective_sparsity is None:
+                self.effective_sparsity = 0.0
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, -1, 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 self.producer is None:
+            try:
+                q_importance, k_importance = self.sparse_token_predictor(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,  # the same single cache
+                    use_cache=use_cache,
+                    layer_idx=self.layer_idx,       # or pass 0
+                )
+                if self.train_headpredictor:
+                    head_importances, past_key_value_hp = self.sparse_head_predictor(
+                        hidden_states,
+                        attention_mask=attention_mask,
+                        position_ids=position_ids,
+                        past_key_value=past_key_value_hp,
+                        use_cache=use_cache
+                    )
+                    head_importances = head_importances.view(bsz, q_len, self.num_heads, self.num_hidden_layers) # [B L H N]
+                q_len = attn_output.size(1)
+                k_len = k_importance.size(-1)
+            except:
+                print(traceback.format_exc())
+                import pdb; pdb.set_trace()
+
+            self.q_importance = q_importance
+            self.k_importance = k_importance
+
+            if self.train_headpredictor:
+                if self.head_importances is None:
+                    self.head_importances = head_importances
+                else:
+                    self.head_importances = torch.cat([self.head_importances, head_importances], dim=1)
+        
+        # if self.layer_idx == 31:
+        #     if q_len == 1:
+        #         self.dtok += 1
+        #         print(f"Primary Key-Value Shape: {past_key_value.predictor_primary_key[0].shape}, Importance: {past_key_value.predictor_importance_key[0].shape}, Tok-Decoded: {self.dtok}")
+        #     else:
+        #         self.dtok = 0
+
+        if not output_attentions:
+            attn_weights = None
+        return attn_output, attn_weights
+
+def convert_kvcache_experimental(model, config, producer_frequency):
+    producer_layer = None
+    producer_layer_device = None
+    layer_counter = {'idx': 0}
+
+    def recurse_convert(parent_module):
+        nonlocal producer_layer
+        nonlocal producer_layer_device
+        for name, module in parent_module._modules.items():
+            if len(list(module.children())) > 0:
+                recurse_convert(module)
+            if isinstance(module, LlamaAttention):
+                device = next(module.parameters()).device
+                dtype = next(module.parameters()).dtype
+                if layer_counter['idx'] % producer_frequency == 0:
+                    new_module = LlamaAttentionExperimental(config).to(dtype).to(device)
+                    producer_layer = new_module
+                    producer_layer_device = device
+                else:
+                    new_module = LlamaAttentionExperimental(
+                        config,
+                        producer=producer_layer,
+                        layer_idx=layer_counter['idx']
+                    ).to(dtype).to(device)
+                new_module.load_state_dict(module.state_dict(), strict=False)
+                is_producer = layer_counter['idx'] % producer_frequency == 0
+                if is_producer:
+                    print(f"Converted Producer layer '{name}' to LlamaAttentionExperimental at layer index {layer_counter['idx']}")
+                else:
+                    print(f"Converted layer '{name}' to LlamaAttentionExperimental at layer index {layer_counter['idx']}")
+                parent_module._modules[name] = new_module
+                layer_counter['idx'] += 1
+    recurse_convert(model)
+    producer_layer = producer_layer.to(producer_layer_device)
+    return model
+
+
+# ---------------------------------------------------------------------
+# 1) Custom Config subclass
+# ---------------------------------------------------------------------
+class LlamaButlerConfig(LlamaConfig):
+    """
+    Extends HF's LlamaConfig to hold optional extra parameters for the "Butler" logic.
+    You can store your custom attributes here, so they can be serialized in config.json.
+    """
+
+    model_type = "llama_butler"
+
+    def __init__(
+        self,
+        eval_llm_mode="ExpPred",
+        token_sparse_method="fixed_50pc",
+        producer_frequency=8,
+        dDash=16,
+        attn_reduce_factor=4,
+        head_attn_reduce_factor=4,
+        intdim=256,
+        flash_attn=False,
+        train_headpredictor=False,
+        min_sparse_index=5,
+        lookahead=0,
+        sliding_window=None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.eval_llm_mode = eval_llm_mode
+        self.token_sparse_method = token_sparse_method
+        self.producer_frequency = producer_frequency
+        self.dDash = dDash
+        self.attn_reduce_factor = attn_reduce_factor
+        self.head_attn_reduce_factor = head_attn_reduce_factor
+        self.intdim = intdim
+        self.flash_attn = flash_attn
+        self.train_headpredictor = train_headpredictor
+        self.min_sparse_index = min_sparse_index
+        self.lookahead = lookahead
+        self.sliding_window = sliding_window
+
+
+# ---------------------------------------------------------------------
+# 2) The main Butler model class
+# ---------------------------------------------------------------------
+class LlamaButlerForCausalLM(LlamaForCausalLM):
+    """
+    A subclass of HF's LlamaForCausalLM that:
+      - Patches each LlamaAttention to your LlamaAttentionExperimental
+      - Sets specialized attributes (eval_llm_mode, etc.)
+      - Overrides _prepare_cache_for_generation to inject PredictorDynamicCache
+    """
+
+    # Let HF auto-detect this config class from config.json:
+    config_class = LlamaButlerConfig
+
+    def __init__(self, config: LlamaButlerConfig):
+        super().__init__(config)
+        """
+        HF's LlamaForCausalLM initializes:
+          self.model = LlamaModel(config)
+          self.lm_head = nn.Linear(...)
+        """
+
+        # 1) Patch the underlying LlamaModel to replace LlamaAttention with LlamaAttentionExperimental
+        self.model = convert_kvcache_experimental(
+            self.model,
+            config,
+            config.producer_frequency
+        )
+
+        # 2) Optionally, set per-module attributes so each LlamaAttentionExperimental knows about them:
+        for module in self.model.modules():
+            if module.__class__.__name__.endswith("AttentionExperimental"):
+                # Set these from your config. Or you can hardcode them if you prefer.
+                module.eval_llm_mode = config.eval_llm_mode
+                module.token_sparse_method = config.token_sparse_method
+                module.set_token_sparsity()  # e.g. sets module.sparse_aggression
+
+                module.producer_frequency = config.producer_frequency
+                module.dDash = config.dDash
+                module.attn_reduce_factor = config.attn_reduce_factor
+                module.head_attn_reduce_factor = config.head_attn_reduce_factor
+                module.intdim = config.intdim
+                module.flash_attn = config.flash_attn
+                module.train_headpredictor = config.train_headpredictor
+                module.min_sparse_index = config.min_sparse_index
+                module.lookahead = config.lookahead
+                module.sliding_window = config.sliding_window
+                module.num_layers_pred = config.producer_frequency  # example usage
+
+                # If this is a "producer layer" (mod.layer_idx % freq == 0), run update_predictor():
+                if hasattr(module, "layer_idx") and (module.layer_idx % config.producer_frequency == 0):
+                    module.update_predictor()
+
+        # 3) Patch the dynamic cache (past_key_values) creation. For your evaluation modes:
+        if config.eval_llm_mode in ["ExpPred", "ReplAttn"]:
+            self._prepare_cache_for_generation = self._patched_prepare_cache_for_generation.__get__(
+                self, self.__class__
+            )
+
+    # -----------------------------------------------------------------
+    # 3) The custom `_prepare_cache_for_generation` override
+    # -----------------------------------------------------------------
+    def _patched_prepare_cache_for_generation(
+        self,
+        generation_config: GenerationConfig,
+        model_kwargs: Dict,
+        *args,
+        **kwargs
+    ):
+        """
+        This override injects a PredictorDynamicCache
+        in place of the standard 'past_key_values'.
+        """
+        if "past_key_values" not in model_kwargs or model_kwargs["past_key_values"] is None:
+            model_kwargs["past_key_values"] = PredictorDynamicCache()
+        return model_kwargs

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+}

special_tokens_map.json

@@ -0,0 +1,16 @@
+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,2061 @@
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+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}