End of training

README.md

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+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- generated_from_trainer
+model-index:
+- name: Mistral_Sparse_refined_web_90p_2024-03-20
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Mistral_Sparse_refined_web_90p_2024-03-20
+
+This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 7.5087
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 0
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- total_eval_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+    "SparseMistralforCausalLM"
+  ],
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+    "AutoModelForCausalLM": "sparsification_sftt.SparseMistralforCausalLM"
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+    0.27582746744155884,
+    0.27582746744155884,
+    0.27582746744155884,
+    0.27582746744155884,
+    0.27582746744155884,
+    0.277833491563797,
+    0.277833491563797,
+    0.35406216979026794,
+    0.5305917859077454,
+    0.8776329159736633,
+    1.0
+  ],
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": false,
+  "use_relu": false,
+  "use_resilu": false,
+  "use_sparse_model": true,
+  "use_sparse_predictor": false,
+  "use_sparse_regularization": false,
+  "vocab_size": 32000
+}

generation_config.json

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

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+}

sparsification_sftt.py

@@ -0,0 +1,1509 @@
+from transformers import TrainerCallback, Trainer
+from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
+from peft import PeftModel
+from datasets import Dataset
+from transformers.utils import is_sagemaker_mp_enabled, is_sagemaker_dp_enabled
+from typing import Any, Dict, Union, Optional, Tuple
+from torch.nn import MSELoss
+from transformers.utils import is_flash_attn_2_available, logging
+import inspect
+import warnings
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+import os
+import copy
+
+from transformers.models.mistral.modeling_mistral import (
+    MistralMLP,
+    MistralAttention,
+    MistralModel,
+    MistralDecoderLayer,
+    MistralConfig,
+    MISTRAL_ATTENTION_CLASSES,
+    MistralRMSNorm,
+    MistralForCausalLM,
+    MistralFlashAttention2,
+)
+from experiments.models.sparse_mistral.svd_router import (
+    low_rank_approximation,
+    SparsePredictor,
+)
+from utils.utils import (
+    print_size_of_model,
+    is_running_deepspeed,
+    is_mainprocess,
+    get_datetime,
+    ds_print,
+)
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(
+        inspect.signature(flash_attn_func).parameters
+    )
+logger = logging.get_logger(__name__)
+
+
+class SparseSFTTTrainer(SFTTrainer):
+    def __init__(self, *args, **kwargs):
+        self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10)
+        self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False)
+        self.use_spm_loss = False
+        self.freeze_original_weights = False
+        self.regularization_type = kwargs.pop(
+            "regularization_type", "L1 positive activation"
+        )
+        assert self.regularization_type in [
+            "L2 activation",
+            "L1 positive activation",
+        ], f"Invalid regularization type: {self.regularization_type}"
+        self.sparse_layers = []
+        self.sparse_decoder_layers = []
+        super(SparseSFTTTrainer, self).__init__(*args, **kwargs)
+
+    def initialize_sparse_silu_layers(self, model):
+        self.sparse_layers = [
+            m for m in model.modules() if isinstance(m, MistralSparseSiluMLP)
+        ]
+
+    def initialize_sparse_decoder_layers(self, model):
+        self.sparse_decoder_layers = [
+            m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer)
+        ]
+
+    def training_step(
+        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+    ) -> torch.Tensor:
+        """
+        Override the huggingface's training_step function to add a regularization term.
+        A regularization term is computed with intermediate values, which are freed after "backward()."
+        You need to set `retain_graph=True` inside `backward` function to keep the values.
+        """
+        model.train()
+        inputs = self._prepare_inputs(inputs)
+
+        with self.compute_loss_context_manager():
+            loss = self.compute_loss(model, inputs)
+
+        if self.args.n_gpu > 1:
+            loss = loss.mean()  # mean() to average on multi-gpu parallel training
+        if not self.freeze_original_weights:
+            if loss is not None:
+                self.accelerator.backward(loss, retain_graph=False)
+
+        if self.use_sparse_regularization:
+            regularization_loss = self.compute_regularization(model)
+            if self.args.n_gpu > 1:
+                regularization_loss = regularization_loss.mean()
+            if regularization_loss is not None:
+                self.accelerator.backward(regularization_loss, retain_graph=True)
+            loss += regularization_loss
+
+        if self.use_spm_loss:
+            spm_loss = self.compute_spm_loss(model)
+            if self.args.n_gpu > 1:
+                spm_loss = spm_loss.mean()
+            if spm_loss is not None:
+                self.accelerator.backward(spm_loss, retain_graph=False)
+            loss += spm_loss
+
+        return loss.detach() / self.args.gradient_accumulation_steps
+
+    def compute_regularization(self, model):
+        """
+        Compute a sparse regularization loss for SiLU
+        """
+        loss = 0
+        if len(self.sparse_layers) == 0:
+            self.initialize_sparse_silu_layers(model)
+        num_layers = len(self.sparse_layers)
+
+        for module in self.sparse_layers:
+            if module.activation_norm is not None:
+                loss += module.activation_norm
+
+        loss /= num_layers
+        loss *= self.regularization_coefficient
+
+        if self.state.global_step % 20 == 0 and loss != 0:
+            print("Negative relularizer loss: ", loss.item())
+        return loss
+
+    def compute_spm_loss(self, model):
+        loss = 0
+        if len(self.sparse_decoder_layers) == 0:
+            self.initialize_sparse_decoder_layers(model)
+        for module in self.sparse_decoder_layers:
+            if module.distill_loss != None:
+                loss += module.distill_loss
+        if self.state.global_step % 20 == 0 and loss != 0:
+            print("Sparse Predictor Distillation loss: ", loss.item())
+        return loss
+
+    # def compute_loss(self, model, inputs, return_outputs=False):
+    #     loss = super().compute_loss(model, inputs, return_outputs)
+    #
+    #     if is_sagemaker_mp_enabled():
+    #         import smdistributed.modelparallel.torch as smp
+    #         @smp.step()
+    #         def smp_forward_backward(model, inputs, gradient_accumulation_steps=1):
+    #             outputs = model(**inputs)
+    #             loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
+    #             loss /= gradient_accumulation_steps
+    #             model.backward(loss)
+    #             return loss
+    #
+    #         loss_mb = smp_forward_backward(
+    #             model, inputs, self.args.gradient_accumulation_steps
+    #         )
+    #         if self.use_sparse_regularization:
+    #             return loss_mb.reduce_mean().detach().to(
+    #                 self.args.device
+    #             ) + self.regularization_coefficient * self.compute_regularization(model)
+    #         else:
+    #             return loss_mb.reduce_mean().detach().to(self)
+    #
+    #     if return_outputs:
+    #         classification_loss, outputs = loss
+    #     else:
+    #         classification_loss = loss
+    #
+    #     loss = classification_loss
+    #     if self.use_sparse_regularization:
+    #         regularization_loss = self.compute_regularization(model)
+    #         loss += self.regularization_coefficient * regularization_loss
+    #
+    #     return (loss, outputs) if return_outputs else loss
+
+
+class SparseTrainer(Trainer):
+    def __init__(self, *args, **kwargs):
+        self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10)
+        self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False)
+        self.use_spm_loss = False
+        self.freeze_original_weights = False
+        self.regularization_type = kwargs.pop(
+            "regularization_type", "L1 positive activation"
+        )
+        assert self.regularization_type in [
+            "L2 activation",
+            "L1 positive activation",
+        ], f"Invalid regularization type: {self.regularization_type}"
+        self.sparse_layers = []
+        self.sparse_decoder_layers = []
+        super(SparseTrainer, self).__init__(*args, **kwargs)
+
+    def initialize_sparse_silu_layers(self, model):
+        self.sparse_layers = [
+            m for m in model.modules() if isinstance(m, MistralSparseSiluMLP)
+        ]
+
+    def initialize_sparse_decoder_layers(self, model):
+        self.sparse_decoder_layers = [
+            m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer)
+        ]
+
+    def training_step(
+        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+    ) -> torch.Tensor:
+        """
+        Override the huggingface's training_step function to add a regularization term.
+        A regularization term is computed with intermediate values, which are freed after "backward()."
+        You need to set `retain_graph=True` inside `backward` function to keep the values.
+        """
+        model.train()
+        inputs = self._prepare_inputs(inputs)
+
+        with self.compute_loss_context_manager():
+            loss = self.compute_loss(model, inputs)
+
+        if self.args.n_gpu > 1:
+            loss = loss.mean()  # mean() to average on multi-gpu parallel training
+        if not self.freeze_original_weights:
+            if loss is not None:
+                self.accelerator.backward(loss, retain_graph=False)
+
+        if self.use_sparse_regularization:
+            regularization_loss = self.compute_regularization(model)
+            if self.args.n_gpu > 1:
+                regularization_loss = regularization_loss.mean()
+            if regularization_loss is not None:
+                self.accelerator.backward(regularization_loss, retain_graph=True)
+            loss += regularization_loss
+
+        if self.use_spm_loss:
+            spm_loss = self.compute_spm_loss(model)
+            if self.args.n_gpu > 1:
+                spm_loss = spm_loss.mean()
+            if spm_loss is not None:
+                self.accelerator.backward(spm_loss, retain_graph=False)
+            loss += spm_loss
+
+        return loss.detach() / self.args.gradient_accumulation_steps
+
+    def compute_regularization(self, model):
+        """
+        Compute a sparse regularization loss for SiLU
+        """
+        loss = 0
+        if len(self.sparse_layers) == 0:
+            self.initialize_sparse_silu_layers(model)
+        num_layers = len(self.sparse_layers)
+
+        for module in self.sparse_layers:
+            if module.activation_norm is not None:
+                loss += module.activation_norm
+
+        loss /= num_layers
+        loss *= self.regularization_coefficient
+
+        if self.state.global_step % 20 == 0 and loss != 0:
+            print("Negative relularizer loss: ", loss.item())
+        return loss
+
+    def compute_spm_loss(self, model):
+        loss = 0
+        if len(self.sparse_decoder_layers) == 0:
+            self.initialize_sparse_decoder_layers(model)
+        for module in self.sparse_decoder_layers:
+            if module.distill_loss != None:
+                loss += module.distill_loss
+        if self.state.global_step % 20 == 0 and loss != 0:
+            print("Sparse Predictor Distillation loss: ", loss.item())
+        return loss
+
+
+class SparseSiLU(nn.SiLU):
+    def __init__(self, threshold):
+        super(SparseSiLU, self).__init__()
+        self.threshold = threshold
+        self.m = nn.Threshold(self.threshold, 0)
+
+    def set_new_threshold(self, threshold):
+        self.threshold = threshold
+        self.m = nn.Threshold(threshold, 0)
+
+    def forward(self, x):
+        act = super(SparseSiLU, self).forward(x)
+        return self.m(act) - self.m(-act)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class SparseMistralAttention(MistralFlashAttention2):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.counts = 0
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+        bsz, q_len, _ = hidden_states.size()
+        mask = abs(hidden_states - hidden_states.mean()) < 0.8 * hidden_states.std()
+        hidden_states[mask] = 0
+        if self.counts <= 1:
+            print(f"Attention {self.layer_idx}: ", (hidden_states==0).float().mean())
+            self.counts += 1
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin, position_ids
+        )
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat(
+                        [attention_mask, torch.ones_like(attention_mask[:, -1:])],
+                        dim=-1,
+                    )
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            (
+                query_states,
+                key_states,
+                value_states,
+                indices_q,
+                cu_seq_lens,
+                max_seq_lens,
+            ) = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(
+                        self.config.sliding_window,
+                        self.config.sliding_window,
+                    ),
+                )
+
+            attn_output = pad_input(
+                attn_output_unpad, indices_q, batch_size, query_length
+            )
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(
+                        self.config.sliding_window,
+                        self.config.sliding_window,
+                    ),
+                )
+
+        return attn_output
+
+    def _upad_input(
+        self, query_layer, key_layer, value_layer, attention_mask, query_length
+    ):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+        )
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim),
+                indices_k,
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
+                query_layer, attention_mask
+            )
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+    # def forward(
+    #     self,
+    #     hidden_states: torch.Tensor,
+    #     attention_mask: Optional[torch.Tensor] = None,
+    #     position_ids: Optional[torch.LongTensor] = None,
+    #     past_key_value: Optional = None,
+    #     output_attentions: bool = False,
+    #     use_cache: bool = False,
+    #     **kwargs,
+    # ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    #     if "padding_mask" in kwargs:
+    #         warnings.warn(
+    #             "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+    #         )
+    #     bsz, q_len, _ = hidden_states.size()
+    #     mask = abs(hidden_states - hidden_states.mean()) < 0.8 * hidden_states.std()
+    #     hidden_states[mask] = 0
+    #     if self.counts <= 1:
+    #         print(f"Attention {self.layer_idx}: ", (hidden_states==0).float().mean())
+    #         self.counts += 1
+    #
+    #     query_states = self.q_proj(hidden_states)
+    #     key_states = self.k_proj(hidden_states)
+    #     value_states = self.v_proj(hidden_states)
+    #
+    #     query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    #     key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    #     value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    #
+    #     kv_seq_len = key_states.shape[-2]
+    #     if past_key_value is not None:
+    #         if self.layer_idx is None:
+    #             raise ValueError(
+    #                 f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+    #                 "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+    #                 "with a layer index."
+    #             )
+    #         kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+    #     cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    #     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+    #
+    #     if past_key_value is not None:
+    #         cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+    #         key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+    #
+    #     # repeat k/v heads if n_kv_heads < n_heads
+    #     key_states = repeat_kv(key_states, self.num_key_value_groups)
+    #     value_states = repeat_kv(value_states, self.num_key_value_groups)
+    #
+    #     attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+    #
+    #     if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+    #         raise ValueError(
+    #             f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+    #             f" {attn_weights.size()}"
+    #         )
+    #
+    #     if attention_mask is not None:
+    #         if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+    #             raise ValueError(
+    #                 f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+    #             )
+    #
+    #         attn_weights = attn_weights + attention_mask
+    #
+    #     # upcast attention to fp32
+    #     attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    #     attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+    #     attn_output = torch.matmul(attn_weights, value_states)
+    #
+    #     if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+    #         raise ValueError(
+    #             f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+    #             f" {attn_output.size()}"
+    #         )
+    #
+    #     attn_output = attn_output.transpose(1, 2).contiguous()
+    #     attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    #
+    #     attn_output = self.o_proj(attn_output)
+    #
+    #     if not output_attentions:
+    #         attn_weights = None
+    #
+    #     return attn_output, attn_weights, past_key_value
+
+
+class MistralSparseSiluMLP(MistralMLP):
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config)
+        self.swish_outputs = None
+        self.relu = nn.ReLU()
+        self.resilu = nn.Sequential(nn.SiLU())
+
+        self.kill_sparse_swish_outputs = False
+        self.dead_percentage = 0
+        self.is_stats = False
+        self.visit_counts = 0
+
+        # Hyperparameters to tune
+        self.dead_threshold = kwargs.pop("dead_threshold", 0)
+        self.pre_mlp_dead_threshold = kwargs.pop("pre_mlp_dead_threshold", 0)
+        self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", True)
+        self.regularization_type = kwargs.pop(
+            "regularization_type", "L1 regularization"
+        )
+        self.regularization_threshold = kwargs.pop("regularization_threshold", 0.5)
+        self.use_relu = kwargs.pop("use_relu", False)
+        self.use_resilu = kwargs.pop("use_resilu", False)
+        self.activation_norm = None
+
+        # Activation Histograms
+        self.is_collect_histogram = False
+        num_bins = 1000
+        self.histogram_bins = torch.linspace(-1, 1, num_bins - 2)
+        self.histogram_bins = torch.cat(
+            [torch.tensor([-torch.inf]), self.histogram_bins, torch.tensor([torch.inf])]
+        )
+        self.pre_mlp_hist_counts = torch.zeros(num_bins - 1)
+        self.pre_act_hist_counts = torch.zeros(num_bins - 1)
+        self.post_act_hist_counts = torch.zeros(num_bins - 1)
+        self.t = 0
+        self.count = 0
+        self.agg_sparsity = 0
+
+        # Sparse activation function
+        self.sparse_act_fn = SparseSiLU(threshold=self.dead_threshold)
+
+    def activate_stats(self, is_collect_histogram: bool = True):
+        self.is_stats = True
+        self.dead_percentage = 0
+        self.visit_counts = 0
+        self.is_collect_histogram = is_collect_histogram
+        self.histogram_counts = torch.zeros(2000)  # .to(self.down_proj.weight.device)
+
+    def deactivate_stats(self):
+        self.is_stats = False
+
+    def collect_stats(
+        self,
+        pre_mlp,
+        pre_activation,
+        post_activation,
+    ):
+        start_time = time.time()
+        pre_activation = pre_activation.float().cpu().detach()
+        post_activation = post_activation.float().cpu().detach()
+        # self.histogram_bins=self.histogram_bins.to(pre_activation.device).type(pre_activation.dtype)
+        self.pre_mlp_hist_counts = torch.histogram(pre_mlp, bins=self.histogram_bins)[0]
+        self.pre_act_hist_counts += torch.histogram(
+            pre_activation, bins=self.histogram_bins
+        )[0]
+        self.post_act_hist_counts += torch.histogram(
+            torch.abs(post_activation), bins=self.histogram_bins
+        )[0]
+        self.t += time.time() - start_time
+        if self.visit_counts % 30 == 0:
+            print(f"Time taken to collect stats: {self.t}s.")
+
+    def forward(
+        self,
+        x,
+        sp_mask: torch.tensor = None,
+    ):
+        """
+        If kill_sparse_swish_outputs is set to False, this layer functions exactly like a normal MLP layer.
+        """
+        if sp_mask != None:  # When sparse mask is given
+            return self.down_proj(
+                self.sparse_act_fn(self.gate_proj(x) * sp_mask) * self.up_proj(x)
+            )  # Todo: This doesn't accelerate runtime (instead slowing down)
+
+        elif self.use_relu or self.use_resilu:
+            if self.use_relu:
+                post_act = self.relu(self.gate_proj(x))
+            else:
+                post_act = self.resilu(self.gate_proj(x))
+            self.count += 1
+            if self.count <= 1:
+                print("USING RELU or ReSiLU!!!!")
+
+            if self.is_stats:
+                dead_neurons = post_act == 0
+                dead_percentage = dead_neurons.float().mean()
+                agg_sparsity = dead_neurons.all(dim=0).float().mean()
+
+                self.dead_percentage = (
+                    self.dead_percentage * self.visit_counts + dead_percentage
+                ) / (self.visit_counts + 1)
+                self.agg_sparsity = (
+                    self.agg_sparsity * self.visit_counts + agg_sparsity
+                ) / (self.visit_counts + 1)
+                self.visit_counts += 1
+
+            return self.down_proj(post_act * self.up_proj(x))
+
+        else:
+            self.count += 1
+            # x[abs(x) < 0.7 * x.std()] = 0
+            if self.count <= 1:
+                print("USING SparseSILU!!!!")
+                # print(x.mean(), x.std(), x.max(), x.min())
+                # print(f"pre mlp sparsity: {(x==0).float().mean()}")
+            pre_act = self.gate_proj(x)
+            post_act = self.act_fn(pre_act)
+            if self.kill_sparse_swish_outputs:
+                dead_neurons = post_act.abs() <= self.dead_threshold
+                # print("pre act sparsity: ", (pre_act==0).float().mean())
+
+                dead_percentage = dead_neurons.float().mean()
+                agg_sparsity = dead_neurons.all(dim=0).float().mean()
+
+                if self.is_stats:
+                    self.dead_percentage = (
+                        self.dead_percentage * self.visit_counts + dead_percentage
+                    ) / (self.visit_counts + 1)
+                    self.agg_sparsity = (
+                        self.agg_sparsity * self.visit_counts + agg_sparsity
+                    ) / (self.visit_counts + 1)
+                    self.visit_counts += 1
+
+                    self.a = dead_percentage
+
+                    # print(self.agg_sparsity)
+
+                    # Collect histogram stats
+                    if (
+                        self.is_collect_histogram
+                        and pre_act.eq(0).float().mean() < 0.99
+                    ):  # Padded dataset
+                        self.collect_stats(x, pre_act, post_act)
+
+                post_act[dead_neurons] = 0
+            if self.count <= 1:
+                print(f"sparsity: {dead_percentage}/ pre-activation sparsity: {(x==0).float().mean()}")
+
+            out = self.down_proj(post_act * self.up_proj(x))
+            if self.use_sparse_regularization:
+                if self.regularization_type == "L1 regularization":
+                    self.activation_norm = torch.abs(post_act)[
+                        post_act < self.regularization_threshold
+                    ].mean()
+                elif self.regularization_type == "L2 regularization":
+                    self.activation_norm = torch.sqrt(
+                        torch.square(post_act)[post_act < self.regularization_threshold]
+                    ).mean()
+
+            return out
+
+
+class SparseMistralDecoderLayer(MistralDecoderLayer):
+    def __init__(
+        self,
+        config: MistralConfig,
+        layer_idx: int,
+        decoder_layer: MistralDecoderLayer,
+        init_svd: bool = True,
+        *args,
+        **kwargs,
+    ):
+        assert isinstance(
+            decoder_layer.mlp, MistralSparseSiluMLP
+        ), f"{type(decoder_layer.mlp)} should MistralSparseSiluMLP."
+
+        super().__init__(config, layer_idx)
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.init_svd = init_svd
+        self.self_attn = decoder_layer.self_attn
+
+        self.mlp = decoder_layer.mlp
+        self.input_layernorm = decoder_layer.input_layernorm
+        self.post_attention_layernorm = decoder_layer.post_attention_layernorm
+
+        # Sparse predictor for mlp (initialized with SVD decomposed matrix)
+        self.low_rank = kwargs.pop("low_rank", 64)
+        self.sparse_act_func = decoder_layer.mlp.sparse_act_fn
+
+        print(
+            f"Setting {layer_idx}th mlp layer's sparse predictor... svd init: {init_svd}"
+        )
+        self.sp_mlp = low_rank_approximation(
+            decoder_layer.mlp.gate_proj,
+            act_func=self.sparse_act_func,
+            init_svd=init_svd,
+        )
+        self.use_async = kwargs.pop("use_async", False)
+        self.use_sparse_predictor = False
+        self.distill_loss = None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        print("hidden_states shape: ", hidden_states.shape)
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+        sp_mask = None
+
+        if self.use_async:
+            sp_mask = self.sp_mlp(hidden_states)
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        if not self.use_async:
+            sp_mask = self.sp_mlp(hidden_states)
+
+        # Compute distillation loss
+        gating_output = self.mlp.sparse_act_fn(self.mlp.gate_proj(hidden_states))
+        loss_func = MSELoss()
+        self.distill_loss = loss_func(sp_mask, gating_output)
+
+        # Convert sp mask into binary form
+        sp_mask = sp_mask > 0
+
+        if self.training:
+            sp_mask = None
+        # if not self.use_sparse_predictor:
+        #     sp_mask = None
+
+        hidden_states = self.mlp(hidden_states, sp_mask)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class SparseMistralConfig(MistralConfig):
+    model_type = "sparse_mistral"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class SparseMistralforCausalLM(MistralForCausalLM):
+    config_class = SparseMistralConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        if config.use_sparse_model:
+            self.apply_sparse_mlp()
+            if config.thresholds is not None:
+                for idx, m in enumerate(self.model.layers):
+                    if isinstance(m.mlp, MistralSparseSiluMLP):
+                        m.mlp.dead_threshold = config.thresholds[idx]
+                        m.mlp.sparse_act_fn.set_new_threshold(m.mlp.dead_threshold)
+                        m.mlp.kill_sparse_swish_outputs = True
+                        m.mlp.use_relu = getattr(config, "use_relu", False)
+                        m.mlp.use_resilu = getattr(config, "use_resilu", False)
+        if config.use_sparse_predictor:
+            self.apply_sparse_predictor(init_svd=config.init_svd)
+
+    def apply_sparse_mlp(self):
+        apply_mistral_sparse_silu_mlp(
+            self,
+            config=self.config,
+            use_sparse_regularization=self.config.use_sparse_regularization,
+        )
+
+    def apply_sparse_predictor(self, init_svd: bool = True):
+        apply_mistral_sparse_decoder_layer(self, config=self.config, init_svd=init_svd)
+
+
+class GracefulRegularizationScheduler(TrainerCallback):
+    def __init__(
+        self,
+        num_warmup_steps=40,
+        is_enabled: bool = False,
+        model_name: str = "mistral",
+        test_dataset: Dataset = None,
+        targeted_sparsity: float = 0.5,
+        keep_regularization_with_kill: bool = False,
+    ):
+        """Scheduler for regularizing the model first before applying the dead threshold.
+
+        :param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40
+        :param increment_ratio: by how much to increase the dead threshold.
+            For example, 0.5 means "increase the threshold by 0.5 * desired threshold
+        """
+        self.num_warmup_steps = num_warmup_steps
+        self.is_enabled = is_enabled
+        self.model_name = model_name
+        self.test_dataset = test_dataset
+        self.targeted_sparsity = targeted_sparsity
+        self.keep_regularization_with_kill = keep_regularization_with_kill
+        self.act_hist_path = (
+            f"/matx/u/vxbrando/histograms/warm_up_reg_{targeted_sparsity}/act_hist.pt"
+        )
+        if self.is_enabled:
+            print("GracefulRegularizationScheduler is enabled.")
+        self.trainer = None
+
+    def set_trainer(self, trainer):
+        self.trainer = trainer
+
+    def on_step_end(self, args, state, control, **kwargs):
+        if not self.is_enabled:
+            return
+
+        model = kwargs["model"]
+        if isinstance(model, PeftModel):
+            base_model = model.get_base_model()
+        else:
+            base_model = model
+
+        if state.global_step == 1:
+            ds_print("Setting an initial reg threshold to 0.1")
+            set_regularization_threshold(base_model, 0.1)
+
+        # if state.global_step >= self.num_warmup_steps and state.global_step % 50 == 0:
+        if state.global_step == self.num_warmup_steps:
+            activate_stats(base_model)
+            enable_sparse_silu(base_model)
+            self.trainer.evaluate()
+            save_act_hist(base_model, self.act_hist_path)
+            set_sparse_threshold(base_model, self.targeted_sparsity, True)
+            deactivate_stats(base_model)
+            self.trainer.use_sparse_regularization = self.keep_regularization_with_kill
+            # set_layer_specific_regularization(model.get_base_model())
+            print_dead_neuron_stats(model.get_base_model())
+
+        if state.global_step % 2000 == 0:
+            if is_mainprocess():
+                ds_print(
+                    f"Saving to /scr/lukeai/{self.model_name}_{state.global_step}.pt",
+                )
+                torch.save(
+                    model.state_dict(),
+                    f"/scr/lukeai/{self.model_name}_{state.global_step}.pt",
+                )
+
+
+class GradualSparsificationScheduler(TrainerCallback):
+    def __init__(
+        self,
+        num_warmup_steps=40,
+        increment_ratio=0.5,
+        is_enabled: bool = False,
+        model_name: str = "mistral",
+    ):
+        """Scheduler for gradually increasing a dead threshold until it reaches the desired threshold.
+
+        :param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40
+        :param increment_ratio: by how much to increase the dead threshold.
+            For example, 0.5 means "increase the threshold by 0.5 * desired threshold
+        """
+        self.num_warmup_steps = num_warmup_steps
+        self.increment_ratio = increment_ratio
+        self.step_size = int(num_warmup_steps * increment_ratio)
+        self.is_enabled = is_enabled
+        self.model_name = model_name
+
+    def on_step_end(self, args, state, control, **kwargs):
+        model = kwargs["model"]
+
+        if not self.is_enabled:
+            if state.global_step <= 10:
+                for module in model.modules():
+                    if isinstance(module, MistralSparseSiluMLP):
+                        module.current_dead_threshold = module.dead_threshold
+            return
+
+        current_dead_threshold = 0
+        desired_dead_threshold = 0
+
+        if is_mainprocess():
+            ds_print(state.global_step)
+
+        if state.global_step % self.step_size == 2:
+            for module in model.modules():
+                if isinstance(module, MistralSparseSiluMLP):
+                    desired_dead_threshold = copy.deepcopy(module.dead_threshold)
+                    current_dead_threshold = module.current_dead_threshold
+                    current_dead_threshold += (
+                        self.increment_ratio * desired_dead_threshold
+                    )
+                    module.current_dead_threshold = min(
+                        desired_dead_threshold, current_dead_threshold
+                    )
+
+            if is_running_deepspeed and is_mainprocess():
+                ds_print(
+                    state.global_step,
+                    current_dead_threshold,
+                    desired_dead_threshold,
+                )
+
+        if state.global_step % 2000 == 0:
+            if is_running_deepspeed and is_mainprocess():
+                ds_print(
+                    f"Saving to /matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt",
+                )
+                torch.save(
+                    model.state_dict(),
+                    f"/matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt",
+                )
+
+
+def get_sparse_mistral_config(
+    config: MistralConfig,
+    use_sparse_model=False,
+    use_sparse_predictor=False,
+    use_sparse_regularization=False,
+    thresholds=None,
+):
+    new_config = SparseMistralConfig()
+    new_config.__dict__.update(config.__dict__)
+    config = new_config
+    config.use_sparse_model = use_sparse_model
+    config.use_sparse_predictor = use_sparse_predictor
+    config.use_sparse_regularization = use_sparse_regularization
+    config.thresholds = thresholds
+
+    return config
+
+
+def apply_mistral_sparse_silu_mlp(
+    model,
+    config,
+    use_sparse_regularization: bool = False,
+):
+    # counts = 0
+    for layer in model.model.layers:
+        # counts += 1
+        # if counts < 4:
+        #     continue
+        original_mlp = layer.mlp
+        new_mlp = MistralSparseSiluMLP(
+            config, use_sparse_regularization=use_sparse_regularization
+        )
+        new_mlp.gate_proj = original_mlp.gate_proj
+        new_mlp.up_proj = original_mlp.up_proj
+        new_mlp.down_proj = original_mlp.down_proj
+        layer.mlp = new_mlp
+
+    # for layer in model.model.layers:
+    #     original_attention = layer.self_attn
+    #     new_attention = SparseMistralAttention(
+    #         config=original_attention.config, layer_idx=original_attention.layer_idx
+    #     )
+    #     for attr in vars(original_attention):
+    #         setattr(new_attention, attr, getattr(original_attention, attr))
+    #         layer.self_attn = new_attention
+
+
+def apply_mistral_sparse_attention(
+    model,
+    config,
+):
+    for layer in model.model.layers:
+        layer.self_attention = layer.self_attention
+
+
+def apply_mistral_sparse_decoder_layer(
+    model,
+    config,
+    init_svd: bool = True,
+):
+    assert isinstance(model.model, MistralModel), "model.model must be a MistralModel."
+    new_layers = []
+    for layer_idx, layer in enumerate(model.model.layers):
+        if isinstance(layer.mlp, MistralSparseSiluMLP):
+            new_layers.append(
+                SparseMistralDecoderLayer(
+                    config=config,
+                    layer_idx=layer_idx,
+                    decoder_layer=layer,
+                    init_svd=init_svd,
+                )
+            )
+            print(f"{layer_idx}th mlp layer activation: {layer.mlp.sparse_act_fn}")
+        else:
+            new_layers.append(layer)
+    model.model.layers = nn.ModuleList(new_layers)
+
+
+def enable_sparse_predictor(
+    model,
+):
+    for layer_idx, layer in enumerate(model.model.layers):
+        if isinstance(layer, MistralDecoderLayer):
+            layer.use_sparse_predictor = True
+
+
+def disable_sparse_predictor(
+    model,
+):
+    for layer_idx, layer in enumerate(model.model.layers):
+        if isinstance(layer, MistralDecoderLayer):
+            layer.use_sparse_predictor = False
+
+
+def activate_stats(model, is_collect_histogram: bool = True):
+    for layer in model.model.layers:
+        if isinstance(layer.mlp, MistralSparseSiluMLP):
+            layer.mlp.activate_stats(is_collect_histogram=is_collect_histogram)
+
+
+def deactivate_stats(model):
+    for layer in model.model.layers:
+        if isinstance(layer.mlp, MistralSparseSiluMLP):
+            layer.mlp.deactivate_stats()
+
+
+def enable_sparse_silu(model):
+    print("Enabling SparseSilu")
+    for i, layer in enumerate(model.model.layers):
+        if isinstance(layer.mlp, MistralSparseSiluMLP):
+            layer.mlp.kill_sparse_swish_outputs = True
+
+
+def print_dead_neuron_stats(model):
+    total_sparsity = 0
+    counts = 0
+    for i, layer in enumerate(model.model.layers):
+        if isinstance(layer.mlp, MistralSparseSiluMLP):
+            dead_percentage = layer.mlp.dead_percentage * 100
+            agg_sparsity = layer.mlp.agg_sparsity * 100
+            print(f"layer {i} sparsity: {dead_percentage:.3f}%")
+            print(f"layer {i} agg sparsity: {agg_sparsity:.3f}%")
+            total_sparsity += dead_percentage
+            counts += 1
+
+    print(f"Total sparsity: {total_sparsity/counts: .3f}%")
+    return total_sparsity / counts
+
+
+def get_sparse_layers(model: MistralModel):
+    sparse_layers = [
+        m.mlp for m in model.layers() if isinstance(m.mlp, MistralSparseSiluMLP)
+    ]
+    return sparse_layers
+
+
+def get_threshold(
+    bin_edges: torch.tensor, histogram_counts: torch.tensor, sparsity_level: float
+):  # Only for L1 Regularization
+    assert (
+        len(bin_edges.shape) == len(histogram_counts.shape) == 1
+    ), "bin_edges and histogram are expected to be 1-dimensional."
+    histogram_counts /= histogram_counts.sum()
+    threshold_idx = torch.searchsorted(
+        histogram_counts.cumsum(0), sparsity_level, side="right"
+    )
+
+    return bin_edges[threshold_idx]
+
+
+def set_regularization_threshold(model, threshold: float = 0.1):
+    for i, layer in enumerate(model.model.layers):
+        if (
+            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
+        ):  # Can set the threshold only the relevant statistics is collected.
+            layer.mlp.regularization_threshold = threshold  # TODO: find better param
+
+
+def set_sparse_threshold(
+    model, sparsity_level: float, use_relu: bool = False, use_resilu: bool = False
+):
+    assert not (use_relu and use_resilu), "It's not allowed to use both relu and resilu"
+    for i, layer in enumerate(model.model.layers):
+        if (
+            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
+        ):  # Can set the threshold only the relevant statistics is collected.
+            if use_relu:
+                layer.mlp.sparse_act_fn = nn.ReLU()
+                layer.mlp.use_relu = True
+                layer.mlp.use_resilu = False
+            elif use_resilu:
+                layer.mlp.sparse_act_fn = nn.Sequential(nn.ReLU(), nn.SiLU())
+                layer.mlp.use_resilu = True
+                layer.mlp.use_relu = False
+            else:
+                layer.mlp.dead_threshold = get_threshold(
+                    layer.mlp.histogram_bins,
+                    layer.mlp.post_act_hist_counts,
+                    sparsity_level,
+                )
+                layer.mlp.sparse_act_fn.set_new_threshold(layer.mlp.dead_threshold)
+                layer.mlp.regularization_threshold = (
+                    layer.mlp.dead_threshold * 1.2
+                )  # TODO: find better param
+
+
+def plot_histogram(
+    bin_edges,
+    histogram_counts: torch.tensor,
+    title: str = "Activation Distribution",
+    fig_dir: str = "figures",
+):
+    plt.bar(
+        bin_edges[:-1], histogram_counts, width=np.diff(bin_edges), edgecolor="black"
+    )
+    plt.title(title)
+    plt.xlabel("Activation Value")
+    plt.ylabel("Frequency")
+    os.makedirs(fig_dir, exist_ok=True)
+    plt.savefig(f"{fig_dir}/{title}.png")
+    # plt.show()
+    plt.clf()
+
+
+def plot_act(model, fig_dir: str = "figures"):
+    for i, layer in enumerate(model.model.layers):
+        if (
+            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
+        ):  # Can set the threshold only the relevant statistics is collected.
+            plot_title = f"Layer: {i} Pre-Activation Distribution"
+            plot_histogram(
+                layer.mlp.histogram_bins, layer.mlp.pre_act_hist_counts, plot_title
+            )
+
+            plot_title = f"Layer: {i} Post-Activation Absolute Distribution"
+            plot_histogram(
+                layer.mlp.histogram_bins, layer.mlp.post_act_hist_counts, plot_title
+            )
+
+
+def save_act_hist(
+    model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt"
+):
+    os.makedirs(os.path.dirname(filename), exist_ok=True)
+    act_dict = {}
+    for i, layer in enumerate(model.model.layers):
+        if (
+            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
+        ):  # Can set the threshold only the relevant statistics is collected.
+            act_dict[i] = (
+                layer.mlp.histogram_bins,
+                # layer.mlp.pre_mlp_hist_counts,
+                layer.mlp.pre_act_hist_counts,
+                layer.mlp.post_act_hist_counts,
+            )
+    print("Saving activation histograms...\n\n\n")
+    torch.save(act_dict, filename)
+
+
+def load_act_hist(
+    model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt"
+):
+    assert os.path.exists(
+        filename
+    ), f"{filename} does not exist when loading pre/post-activation histogram of SparseMistralSiluMLP."
+    print("Loading activation histograms...\n\n\n")
+
+    act_dict = torch.load(filename)
+    for i, layer in enumerate(model.model.layers):
+        if (
+            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
+        ):  # Can set the threshold only the relevant statistics is collected.
+            (
+                layer.mlp.histogram_bins,
+                # layer.mlp.pre_mlp_hist_counts,
+                layer.mlp.pre_act_hist_counts,
+                layer.mlp.post_act_hist_counts,
+            ) = act_dict[i]
+
+
+def enable_last_k_modules(model, start_module_idx: int):
+    assert 32 > start_module_idx >= 0
+    new_modules = []
+    new_idx = 0
+    for idx in range(start_module_idx, len(model.model.original_layers)):
+        module = model.model.original_layers[idx]
+        module.layer_idx = new_idx
+        module.self_attn.layer_idx = new_idx
+        new_modules.append(module)
+        new_idx += 1
+        print(module.layer_idx)
+
+    model.model.layers = nn.ModuleList(new_modules)
+
+
+def enable_first_k_modules(model, end_module_idx: int):
+    assert 32 > end_module_idx >= 0
+    new_modules = []
+    new_idx = 0
+    for idx in range(0, end_module_idx + 1):
+        module = model.model.original_layers[idx]
+        module.layer_idx = new_idx
+        module.self_attn.layer_idx = new_idx
+        new_modules.append(module)
+        new_idx += 1
+        print(module.layer_idx)
+
+    model.model.layers = nn.ModuleList(new_modules)