Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing
Authors: Qi Le
Enmao Diao
Ziyan Wang
Xinran Wang
Jie Ding
Li Yang
Ali Anwar
Venue: ICLR 2025 Poster (2025)
Keywords: Large Lanuage Model Pruning
Probe Pruning
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner.

Abstract: We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight that not all samples and tokens contribute equally to the model's output, and probing a small portion of each batch effectively identifies crucial weights, enabling tailored dynamic pruning for different batches. It comprises three main stages: probing, history-informed pruning, and full inference. In the probing stage, PP selects a small yet crucial set of hidden states, based on residual importance, to run a few model layers ahead. During the history-informed pruning stage, PP strategically integrates the probing states with historical states. Subsequently, it structurally prunes weights based on the integrated states and the PP importance score, a metric developed specifically to assess the importance of each weight channel in maintaining performance. In the final stage, full inference is conducted on the remaining weights. A major advantage of PP is its compatibility with existing models, as it operates without requiring additional neural network modules or fine-tuning. Comprehensive evaluations of PP on LLaMA-2/3 and OPT models reveal that even minimal probing—using just 1.5% of FLOPs—can substantially enhance the efficiency of structured pruning of LLMs. For instance, when evaluated on LLaMA-2-7B with WikiText2, PP achieves a 2.56 times lower ratio of performance degradation per unit of latency reduction compared to the state-of-the-art method at a 40\% pruning ratio.

Published as a conference paper at ICLR 2025
PROBE PRUNING: ACCELERATING LLMs THROUGH DYNAMIC PRUNING VIA MODEL-PROBINE
Qi Le', Enmao Diao, Ziyan Wang", Xinran Wang', Jie Ding', Li Yang', Ali Anwar' University of Minnesota University of North Carolina at Charlotte (1e000288, wang8740, dingj, aanwar) @umneeuu. diao_em@hotmail com, (zwang53, 1yang501@charlosttto @charlttteeeddeeeeeeooooo
ABSTRACT
We introduce Probe Pruning (PP), a novel framework for online, dynamic, struc- tured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight that not all samples and tokens contribute equally to the model's output, and probing a small portion of each batch effectively identifies crucial weights, enabling tailored dynamic pruning for different batches. It com- prises three main stages: probing, history-informed pruning, and full inference. In the probing stage, PP selects small yet crucial set of hidden states, based on residual importance, to run a few model layers ahead. During the history-informat pruning stage, PP strategically integrates the probing states with historical states. Subsequently, structurally prunes weights based on the integrated states and the PP importance score, metric developed specifically to assess the importance of each weight channel in maintaining performance. In the final stage, full in- ference is conducted on the remaining weights. A major advantage of PP its compatibility with existing models, as it operates without requiring additional neural network modules or fine-tuning. Comprehensive evaluations of on LLaMA-2/3 and OPT models reveal that even minimal probing-using just 1.5% of FLOPs-can substantially enhance the efficiency of structured pruning of L.LMs. For instance, when evaluated on IJ .LaMA-2-77 with Wiki'Text2, PP achieves a 2.56x lower ratio of performance degradation per unit of runtime reduction compared to the state-of-the-art method at 40% pruning ratio. Our code is available at www.co//////// /01-001000111 Le Letpa-eeeePeeeeeeeeeee Prouiin_r_nng
INTRODUCTION
Large Language Models (LLMs) Vaswaani et al. 2017 Zhang et al. 2022; Touvron et al. 2023
Diao al. 2024p have recently achieved significant success, leading to the development of numerous applications (OpenAl 2023 Anand et al., 2023). However, the inference for these models, often containing billions of parameters, presents challenges. These challenges primarily arise from the substantial computational demands and the risk of high latency (Ma et al. 2023).
Structured pruning is promising hariwlle- approach reduce computational consumption and accelerate inference (Yuan et al. 2021). It removes complete structures from models, such as weight channels and attention heads. Compared with other methods like unstructured pruning (Frantar
& Alistarh| 2023; Sun et al. (023),, parameter sharing (Diao et al. (001),, offloading (Rasley et al.
2020 Diao al.) 2022. 2024), and quantization (Dettmers et al. 2022 Lin et al. 2023] Frantar
et al. 2022). structured pruning reduces computational resources and speeds up inference without
requiring specific hardware. However, when applied LLMs, structured pruning often results in performance gap compared to dense models (Wang et al. 2024b).
major factor contributing the performance gap in L.LMS may be the emergence significant outlier phenomena in internal representations (Dettmers et al.) 2022 Liu et al. 2024 Sun et al. 2024). Current advanced structured pruning methods typically utilize calibration datasets to assess the importance of weights using pruning metrics. For example, the FLAP method (An et al. 2024) uses calibration dataset to compute fluctuation metrics for each input feature and its corresponding channel in attention or MLP block weight matrices, specifically in the output projection (0) fully connected layer 2 (FC2). Similarly, order Taylor expansions of the error function, calculated using calibration dataset, eliminate the
(Ma et al. (023) employs approximated second-

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least important coupled structures. Although the calibration dataset provides valuable insights for pruning by identifying non-critical weights, this approach overlooks the batch- setch-dependent nature of
outlier properties in LLMs (Liu et al., 2023b] Song al. 2023 Liu et al. 2024 Sun et al. 2024). which vary across different input batches and cannot be accurately predicted prior to inference. Experimental illustrations can be found in Appendix [B.3] Consequently, pruning decisions based solely on calibration dataset may fail to address these dynamic outliers during real-time inference, resulting in suboptimal model performance. Fine-tuning can serve a method recover model performance (Wang et al., 2024b), but it is resource-intensive and may be impractical for certain real-world applications.
To effectively handle batch-dependent outliers and reduce the performance gap between pruned and dense models without extensive fine-tuning, we propose Probe Pruning (PP). PP is an online dynamic structured pruning framework that prunes the model during inference based on each batch's 's hidden states. Notably, PP relies solely on the original model structure and hidden states, without requiring additional neural network modules or fine-tuning. We overcome two key challenges:
Leveraging Calibration Dataset may Introduce Biases: Relying exclusively on the calibration dataset may introduce biases, as the pruned channels are entirely determined by the calibration dataset. For example, when FLAP used the WikiText2 validation set as calibration dataset, it achieved a perplexity of 18.5 on the WikiText2 test set of LLaMA- calibration dataset.
2-7B with a 40% pruning ratio. In contrast, using the C4 dataset the perplexity increased to 38.9 on the WikiText2 test set. We propose distory-informer pruming with importance-scaled fusion to leverage the benefits of the calibration dataset while minimizing associated biases.
Dynamic Pruning Without Access to Intermediate Hidden States: Deciding online which channels to prune during inference for each batch is challenging. Without gradients, calculating pruning metrics for attention and MLP blocks requires intermediate hidden states, which are the input tensors to the attention output projection and MLP's FC2 layer. These states are unavailable when the input hidden states initially enter these blocks. Moreover, not all samples and tokens contribute equally to the model's s output, and large-magnitude outliers in LLMs often have significant impact on the model's 's behavior. Therefore, we propose a probing method that selects key samples and tokens from the input hidden states, runs few model layers ahead, and obtains intermediate hidden state information. Without such probing, accessing intermediate hidden states requires significant computational costs.
Specifically, PP leverages a small yet crucial segment of hidden states to run a few model layers ahead and capture the probe's 's intermediate hidden states, which contain essential information for guiding pruning decisions within the attention or MLP blocks of the current batch. By strategically integrating the probing states with historical states, we can dynamically determine which channels to prune. After pruning the weight channels, we run full inference on the remaining weights. Furthermore, our probing is minimal yet effective: for example, it operates with only 5% of the samples and 50% of the tokens, utilizing just 1.5% of the floating point operations (FLOPs) of dense model inference, and yet it has proven effective. Experimental results confirm that this minimal probe effectively captures critical intermediate hidden state information.
RELATED WORK
Pruning with Calibration Dataset. Pruning methods can be broadly classified into two cate-
gories YYuan et al. 2021): unstructured praning and structured pruning. Unstructured pruning (Le-
Cun et al.
1989 Hassibi et al. 1993) Han et al.) 2015 Mushtaq et al. 2021: Li et al. 2021 Soltani
al.
2021 Yang et al. 2022, Diao et al.) 2023 Liu et al. 2023at] Li et al. 2024b a Dong et al.
2024 removes individual weights, whereas structured pruning (Li et al. [016]]
Liu et 2017 He
et al. 2019, Diao et al. 2020 |Fnn et al. 2023) removes complete structures from the model, such as
channels and attention heads. Pruning LLMss often involves calibration datasets due to the emergence of outliers in their internal representations. For unstructured pruning, SparseGPT (Frantar & Alistarh 2023) uses synchronized second-order Hessian updates to solve row-wise weight reconstruction prob- lems and update weights. Wanda (Sun et al. (023) introduces a pruming metric that considers both the magnitude of weights and activation values to determine which weights to prune. For structured pruming, FLAP (An et al. 2024) introduces a fluctuation metric to decide which weight channels to prune. L.LMMPruner (Ma et al. 2023) employs approximated second-order Taylor expansions of the
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Residual
Probing
3
Historical
Importance
States
0
2
States
(1) Probing
Residuals
Pruning & Full Inference
History-Informed
Pruning
Probe Run
4. Full Inference
2.5
Generation
2
2.6
Soyer-Normalized
Bayer-Normalized
Hidden States
Hidden
Figure 1: Probe Pruning (PP) is executed in four stages: (1) PP selects key samples and tokens hidden states, based on residual importance, to create a small yet crucial few model layers ahead and obtains the probe's 's intermediate hidden states. (3) PP integrates the probing states with historical states and uses the integrated states caleulate the pruning metric and prune weight channels. (4) PP performs full inference on the remaining weights.
from the
probe. (2) PP deploys this probe
error function to remove the least important coupled structures and then applies fine-tuning to recover model performance. LoRA-Prune (Zhang et al. 2023) a LoRA (Hu et al., (2021)-guide pruning metric that leverages the weights and gradients of LoRA to direct the iterative process of pruning and tuning. However, the fine-tuning process requires substantial computational resources (Hoffmann) et al., 2022), and we have found that fine-tuning might cause LLMS to lose their generalizability: for example, they may perform worse on certain downstream tasks, such as commonsense reasoning tasks.
Large-Magnitude Outliers of LLMs. Unlike small neural networks, LLMs exhibit large- magnitude outlier features (Kovaleva al.) [021] Dettmers et al.] 2022 2023 Schaeffer al.) 2024; Sun et al. 2024). Dettmers et al. (2022) shows that these large-magnitude features begin emerge when the size LLMs exceeds 6.7 billion parameters, and these outlier features concentrated in certain channels. The phenomenon of massive activations (Sun et al. 2024 Liu
al. (024)) has been observed, where a few activations exhibit significantly larger values than others. potentially leading to the concentration of attention probabilities on their corresponding tokens. These emergent properties suggest the need to customize the pruning of channels for different batches to maintain model performance. This observation motivates us to propose Probe Pruning.
NOTATIONS AND PREIIMINARIES
An LLM M consists of blocks, each of which can be either an attention block or a Multi-Layer Perceptron (MLP) block. Each attention block is characterized by four linear projections: Query (Q), Key (K), Value (V), and Output (O), Similarly, each MLP block includes two linear layers: Fully Connected layer (FC1) and Fully Connected layer 2 (FC2).
Each block transforms the input hidden state X IR Nx into the output hidden state X Nx5xD Here, N, S, and D denote the batch size, sequence length, and feature dimension, respectively. The transformations each block can be expressed as:
x X + (X)).
where Fl encompasses all transformations within block I. This function can be further decomposed
xl.int (i-liiilll
x
(LN'(X)))
(2)
where T represents all intermediate transformations applied to the input hidden state x' excluding
the layer normalization LN and final weight matrix W/l.inal R CCn The final weight matrix either the Output projection (O) an attention block or FC2 in MI.P block. The intermediate hidden state iint XSSCC, results from applying these intermediate transformations x!
Additionally, we define the residual importance as the L norm the input hidden states X' across specific dimensions, concept further detailed in Section 4.2

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In structured pruning of LLMs, entire coupled structures pruned (Ma et al. 2023 An et al. 2024). Specifically, in block I, the preceding weight matrices are adjusted by pruning their output channels, which correspond one-to-one with the input channels pruned by the final weight matrix. For example, in an MIP block, the weight matrices are adjusted based on the set of unpruned channel indices C! C (1,2..... ... follows:
W w. .), W. w CJ,,
where WT.FFI E R and W7 E RCuxxicl, The notation |C|| represents the cardinality of C² Similarly, in the attention block, attention heads can be treated as coupled structures (Ma et al. An et al. 2024), and entire attention heads are pruned.
2023
4 METHODOLOGY
The objective of Probe Pruning (PP) is to implement online dynamic structured pruning in a batch- wise manner. The main idea of our work is illustrated in Figure [ Our core strategy involves: (1) Probing (Sections 4.1 and 4.2) which consists of two steps: first, generating probe based on residual importance; second, using the probe to run the unpruned model to gather valuable intermediate hidden state information. (2) History-informed pruning (Section 4.3p, which carefully merges the probing states with historical states using importance-scaled fusion to capture the essential characteristics of each batch. Afterward, we prune the model using novel pruning metric (Section 4.4 effectively selects channels for pruning than existing metrics.
that more
4.1 PROBING
We introduce a novel concept called probing, which leverages the existing model structure and hidden states to form predictive mechanism. Specifically, when the input hidden states reach block L, probing first utilizes residual importance select key samples and tokens, forming the probe p from LN'(X') LN' represents the layer normalization at block l. The process of probe generation is detailed in the next section. It then runs the intermediate transformation in block I, denoted by ('(P). Notably, effective probing consumes few computational resources and can obtain important intermediate-state information to guide pruning decisions.
Upper Bound of Probing. As an alternative, we can generate the probe by using all the input hidden states in the current batch, p' = LN (x'), method refer as Full-Batch Probing. By utilizing the entire batch without reducing the dimensions N or S, Full-Batch Probing captures the complete intermediate hidden state information, which could potentially lead to optimal pruning performance. However, this approach significantly increases computational resource requirements and latency. Therefore, Full-Batch Probing serves as theoretical upper bound for our method. Our aim for PP is to select pruning channels similar to those chosen by Full-Batch Probing. We believe that a higher proportion of common pruning channels between PP and Full-Batch Probing indicates better model performance and higher quality of the probe.
Why Does Probing Work? Probing is effective because not all samples and tokens contribute equally the model's output, and aarge-manitude outliers in LLMS significantly influence the model's behavior. In natural language sequences, certain tokens carry more semantic or syntactic significance than others (Xiao et al. 2023 Sun et al. 2024 Liu al., 2024). By selecting key samples and tokens based on residual importance, the probe focuses on the most informative parts within the batch. This targeted approach allows the probe to capture essential intermediate hidden state information that is most influential in determining which channels can be pruned. Consequently, even though the probe processes reduced subset of the batch, provides sufficient insight to guide pruning decisions, potentially achieving results comparable to Full-Batch Probing with significantly lower computational costs.
Computational Complexity. Only minimal computational complexity is required for probing. Specifically, for an LLM characterized by six linear transformations per attention and MLP block (Q/K/V/O and FC1/FC2) that incorporate weight transformations and the attention mechanism, the dense matrix computational complexity for an LLM totals O(6N SC,, Com + +2N For probing, by reducing the batch size to x% and the sequence length 4% of their original sizes, the complexity réduces to (44%% y% NS Com + 2x% (v%)² C
4.2
PROBE GENERATION
PP measures the residual importance, which is the L norm of X across specific dimensions to identify key samples and tokens. Once identified, these key samples and tokens are selected from
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Algorithm 1: Probe Pruning
Input: An LLM M with T. blocks, cach containing the Transformation F¹, the Intermediate transformation T. and Layer Normalization LNN; calibration dataset D); Inference hatches B.
System executes:
Run the calibration dataset using model MM to obtain historical states for i-th batch H' do
Initialize the hidden state X for batch B
for each block - 0, .....
L , do Generate a probe P' from LN (x'), utilizing residual importance (Section 4.2) Use P' execute the intermediate transformation of block and gather the resulting intermediate hidden stales, denoted as X pi..eeeoobee T (P').
Use importance-scalee fusion to integrate the probing states X'I.im.probb with historical states
(Section 4.3)
Compute the PPsp pruning metric from the integrated (Section 4.4 and subsequently
prune the weight channels accordingly.
Execute full inference on X' using pruned weights W denoted by F F((X)).
end
end
LN (X') generate probe for block I, where LN' denotes layer normalization at block I. We do utilize the importance derived from LN (X') identify key samples and tokens because layer normalization substantially alters the input hidden states.
To measure the residual importance along the target dimension, we compute the I norm of X across non-targed dimensions. The target dimension may be either the batch or sequence dimension.
U hhich
|X S....lll
for i 1....,N. N,
(4)
X ,,,2,,
for j 1, I.
(5)
After computing the importance scores, we sort them in descending order and store the indices in II:
Il butch argsort(
pl.eeq argsort se4).
(7)
Using the sorted indices, we then generate the probe by selecting the top x% of samples or y% of tokens from the luyer-normalize X":
LN (X)) P LN
if selecting top x% of samples,
p
25%
(8)
if selecting top y% tokens.
This method ensures that the probe consists of the most significant samples and tokens, as ranked by their importance scores.
PP implements sequential approach to prune both sequence and batch dimensions effectively. Initially, the top y% of tokens are selected from the residual X guided by Eqs. 5 and 7 leveraging the sequence distribution within the current batch: x' plase Subsequently. we apply this reduced
sequence set determine the top x% of samples using Eqs. and 6 resulting in the indices bsugchhsse Finally, we select the key samples and tokens for probe generation as LN (x)
4.3 HISTORY- DFFORMEDD PRUNING WITH IMPORTANCE- SCALED FUSION
The intermediate hidden states of the probe, given by
x int, = T (P')
contain crucial information that guides pruning decisions. However, when the probe is very small for instance, when N and S are reduced to 5% might lead inappropriate pruning decisions due

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to limited context. To address this issue and enhance performance, introduce history-informer pruning with importance-scaite fusion.
To simplify notation, omit the superscript I, which denotes the block number, in this section. For intermediate hidden states n of shape (N, S, Cin) the following relationship holds:
int (.
Σ
X Likk
|| x
(10)
We compress the batch dimension in the first step of Eq. 10 to store historical states because memory limitations prevent storing the intermediate hidden states of all samples. We sum over the sequence dimension in the second step of Eq. 10 to obtain the tensor in shape R which is used to compute the pruning metric (see Section
As in previous studies (Sun et al. 2023 An et al., 2024). we process the calibration dataset D using the model M to obtain initial historical states. For each block, initial historical states are represented by
VI E IR SxCa, computed as the first step of Eq. 10 to reduce the batch dimension: V -p=10 | J.
EN (Ximl w. Similarly, to reduce the batch dimension of probe's intermediate hidden states %%
probe 1=*%NNx6%.SXC., we calculate probing states as |
-: (Kijik (xint.mhee)..
Importance-Scales Fusion. Since probing can be performed with selected tokens, is necessary to align the sequence dimension. We define Vnmobe -Vra yt where Vambe E TRv% and They
3/%
obtained from Eq. 7 represents the indices of the top y% of tokens. We then apply importance-scaled fusion to obtain integrated states:
probe J,k
Vamoe
probe
Vprabe
(11)
probe J.k
Vmrahe
X
probe jiik
ynooee
where X intpprobe
E Sx
Following the second step of Eq.
10 we sum X
intbp
over the sequence pnobe
dimension to obtain Ey
int.prohe
Note that without importance-scaled fusion, Σj X
j.k
can reduce 1 Then, calculate the pruning metric based on Eq. (15), and prune the weight channels accordingly. Finally, we run full inference on the remaining weights.
Wi and Σj=1 j.k as surrogate of X | to
ini.
Update Historical States with Full Inference.
To enhance the tracking of intermediate hidden state attributes, we implement exponential moving average during full inference on the selected weight channels C. The update formula expressed as:
V Vecll cV + X)]] x 3.cl
(12)
The value of V is updated for 1-th inference batch, and x int during full inference. We consistently set À 0.99 across all implementations.
represents the intermediate hidden states
4.4 PRUNING METRIC
We propose new structured pruning metric named PPsp, where "sp" stands for structured pruning. This metric more effectively selects channels for pruning compared to existing metrics. We adapt the unstructured pruning metric Wanda (Sun et al. 2023) to structured pruning scenario. PPsp introduces two enhancements: (1) we preserve the inherent importance of individual weights, as represented by the squared value of the Wanda metric; and (2) calculate the 1,2 norm of the importance scores for MLP input channels and attention heads to determine the pruning structures' importance, rather than summing these scores across pruning structures.
We introduce the pruning metric for general scenario. To enhance clarity, we omit the superscript 4, which denotes the block number, in this section. At each block, given intermediate hidden states Xinl of shape (NV, S, Cmn). where N and S represent the batch and sequence dimensions respectively, and the weight matrix Wifllal of shape (Cont, Cin), Wanda (Sun et al. 2023) defines the importance of the individual weight Wifnnal as: i,k
1
W/finaal
[ xi..lll
(13)

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Published as a conference paper at ICLR 2025
where| denotes the absolute value operation, and |X__ [x,,,lllz evaluates the L norm of the kth feature across the S)) dimensions. These two scalar values are then multiplied to produce the final importance. However, as derived in Wanda (Sun et al. 2023), the inherent importance of an individual weight is defined by:
....k 1l2) 1
(14)
Wanda discards the squaring in Eq. [14p in local weight importance ordering, as the non-negative nature of W/ and | ا|", does not impact the relative ordering of importance. However, when 1.A
comes to structured pruning, maintaining the inherent importance of individual weights essential. Thus, we square the Wanda metric and compute the Euclidean distance across the Cout dimension of
the input channel. The formula is given by:
1.
W final
int
Cur
(15)
i=
where {.} signifies the set of elements, and E RCa.
EXPERIMENTAL SETUP
Table Comparison LLM structured pruning methods. Our implementation loads the full model for dynamic pruning, while other methods load only the pruned version.
Method
Fine-tuning
Time-Efficient
Easy Integration
Dynamic Pruning
Wanda sp FLAP
LLM- Pruner
X
LoRAPrune
PP
We conduct three experiments using different random seeds for all tests and show the standard error across these three seeds in brackets. We conduct all experiments NVIDIA A100 GPUs.
Models and Evaluation. We evaluate PP on three popular model families: I.LMA-2 7B/13B Touu
vron et al. 2023), ILaMA-3 8B (Meta Al 2024). and OPT-13B (Zhang et al.
2022). Following previous work (Sun et al.] 2023 An et al. 2024), we evaluate the models on two zero-shot task categories. We evaluate accuracy commonsense reasoning tasks, including BoolQ (Clark et al.
2019). PIQA (Bisk et al., 2020), HellaSwag (Zellers et al. 2019). WinoGrande (Sakaguchi et al.
2019
ARC ARC-Easy (Clark et al. 2018), ARC-Challenge (Clark et al. 2018), and OpenbookQA (Mi-
haylov et al. 2018). For evaluating perplexity on the text generation task, we use WikiText2 (Merity al. 2016). We set the batch size 20 for all tasks. For the commonsense reasoning tasks, our implementation follows (Gao et al. 2021), ), setting the sequence length of each batch to match its longest sample. For the text generation task, we set the sequence length to 1024. For PP, we set the default probe size to 5% of the batch size and 50% of the sequence length, approximating 1.5% of the FLOPs cost relative dense model inference. Figure 3 shoows ablation study results for various probe combinations, indicating small probes enhance model performance. Ablation studies of the PP and FLAP are available in Appendix and additional experimental results are available Appendix
Baselines.
We compare our method, PP, with four previous approaches: Wanda-sp (An
2024
FLAP (nn et al. 2024), LoRAPrune (Zhang et al. (202)). and LLM-Pruner (Ma et al. 2023). We also compare PP with upper bound, Full-Batch Probing, as introduced in Section [. Following (Sun) et al. 2023 An et al., (002),, we use the C4 (Raffel et al. 2020) dataset as the calibration dataset for all methods. use 2,000 calibration samples for PP, Wanda-sp, and FLAP, and 20,000 calibration samples for tuning LoR Prune and LLM-Pruner. We evaluate pruning ratios of 20% and 40%.
RESULTS
Main Results. We present the zero-shot performance, without fine-tuning, of four models on text generation and commonsense reasoning tasks, shown in Tables 2 and 3 Probe Pruning (PP) consistently outperforms all baselines across various models and pruning ratios. For instance, on WikiText2 a 40% pruning ratio, PP achieves lower perplexities than competing methods: 16.8 with L.L.MA---7B, 11.3 with LLaMA-2-13B, and 26.7 with OPT-13B. Moreover, PP attains significantly lower perplexities and higher reasoning task accuracies than both LLM-Pruner and LoRAPrune. For example, on LLaMA-2- 13B at a 40% pruning ratio, PP achieves an average accuracy of 61.0%, significantly higher than 52.0% for LL-M-runer and 48.1 % for LoRAPrune. On L.AMAA3-8BB
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Table 2: Zero-shot performance of LLMMA-2-7B/113 and OPT-13B after pruning attention and MLP blocks without fine-tuning: PP demonstrates superior performance in nearly all scenarios. Arrows indicate metric direction (4: lower better; t: higher is better).
Text Generation
Commonsense Reasoning
Mothod
Pruning Ratio
LLaMA-2-7B
LLaMA-2-13B
OPT-13B
LLaMA-2-7B
LLaMA-2-138
OPT-13B
0%
6.0(0.1)
5.1(0.1)
11.6(0.1)
66.2
Full-Batch Probing
20%
7.3(0.1)
6.200.1)
12.6(0.1)
56.4
Wande-sp
20%
10.6(0.1)
9.000.1)
17.4(0.1)
55.2
FLAP
20%
10.3(0.1)
7.5(0.1)
18.8(0.))
64.6
54.9
LoRAPrune wio LoRA
20%
22.7(0.9)
16.1(0.7)
57.9
58.9
LLM- Pruner who LoRA
20%
17.5(166
11.3(0.7)
57.4
61.3
PP
20%
8.1(0.1)
6.7(0.1)
14.7(0.1)
62.8
65.3
56.5
Full-Batch Probing
40%
13.6(0.1)
8.9(0.1)
17.9(0.2)
58.7
62.9
$4.0
Wanda-sp
40%
43.8(1.5)
21.6(0.4)
42.7(0.7)
$4.8
56.6
50.5
FLAP
40%
38.9(1.3)
15.5(0.0)
51.0(0.7)
$4.9
60.6
50.8
LoRAPrunce wio LoRA
40%
129.5(3.0)
74.8(6.4)
45.4
48.1
L.M. Pruner w/o LoRA
40%
(1.1(4.3)
34.5(2.4)
47.8
52.0
PP
40%
16.8(0.1)
11.30.11
26.7(0.3)
56.6
61.0
S3.1
Table Zero-shot performance of LLaMA-3-8B after pruning MLP blocks without fine-tuning: PP demonstrates superior performance in nearly all scenarios.
Method
Pruning Ratio
WikeiTeu22
BoolQ
PIQA
HellaSwag
WinoGrande
ARC-c
ARCle
OBQA
Dense
0%
6.8(0.0)
81.70.0)
79.5(0.0)
76.3(0.0)
72.5(0.0)
47.2(0.0)
61.7(0.0)
40.2(0.0)
65.6
Full Butch Probing
20%
8.5(0.0)
79.000.0)
80.1(0.0)
74.8/0.0)
73.9(0.0)
44.9(0.0)
60.7(000)
(0.2/0.0)
61.8
Winds-sp
10,00.00
75.100.3)
78.500.0
71,400.4
38.7004)
56,900.4
39.000.1
FLAP
20%
10.000.0)
79.4(0.2)
78.7(0.1)
70.3(0.0)
71.4(0.5
408((011)
57.8(000)
39.4(0.3)
62.5
20%
9.3(0.0)
77,4(0.0)
78.5(0.0)
73.1(0.0)
72.5(0.3)
43.2(003)
59.1(0.2)
40.2/0.5)
63.4
Batch Probing
12.3/0.1)
73.100.0)
77.8(0.0)
70.5(0.0)
70.3(0.0)
42.9(0.0)
58.9(0.0)
39.8/0.0)
Windl-sp
40%
18.4(0.1)
66.600.1)
73.400.2)
56.70.1)
63.200.2)
31.8(0.))
47,001.5)
34,5(0.2)
53.3
PLAP
40%
18.5(0.2)
67.3(1.0)
73.5(0.0)
57.2/0.2)
66.7(0.5)
31.7(0.3)
44.6(0.3)
44.4))
53.6
70.3(0.1)
76.3(0.2)
65.20011)
67.2/0.2)
39.0(0.3)
57.4(0.1)
36.90.33
PP surpasses Wanda-sp and FLAP in nearly all tasks, confirming its effectiveness and robustness. For instance, at a 40% pruning ratio, PP achieves an average accuracy of 58.9%, outperforming Wanda-sp (53.3%) and FLAP (53.6%). In Section 4.1 we stated that Full-Batch Probing represents the upper bound of PP. Experimental results confirm that Full-Batch Probing excels in all tested scenarios, supporting our hypothesis. Compared to Full-Batch Probing, which requires significant extra computational resources more than dense model inference PP achieves comparable results while utilizing minimal computational resources, only 1.5% of the FLOPs compared to dense model inference. These results imply the effectiveness of PP and demonstrate that the probe's intermediate hidden states can help identify the important weights for processing different batches.
PP
wi PP wil PP
ve PV wio
woo PP
vplo PP
Layer Ooder Attention, WikiText2
Layer Onler (h) ML.,, WikiTex22
Layer Order
Layer Urder (d) MLP. ARC-
Attention, ARC-e
Figure 2: Jaccard Index of common pruning channels: comparing PP and Full-Batch Probing, and comparing fix- fix-pruned model (without PP) and Full-Batch Probing for each batch.
Jaccard Index of Common Pruning Channels. To verify our assumption Section 4.1 that greater overlap of pruning channels between PP and Full-Batch Probing correlates with enhanced model performance and probe quality, we measure the Jaccard Index (Jaccard 1912) of common pruning channels in two comparisons: between PP and Full-Batch Probing, and between the fix- pruned model (without PP) and Full-Batch Probing. The Jaccard Index is a statistical measure of the similarity between two sets, defined as the size of their intersection divided by the size of their union. We consistently apply the PPsp metric in all comparisons. As shown in Figure 2 PP consistently selects pruning channels more similar to those selected by Full-Batch Probing across almost all
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Published as a conference paper at ICLR 2025
attention and MLP blocks, in contrast to the fix- pruned model (without PP). This increased alignment of channels contributes to improved overall performance and indicates that the probe's 's intermediate hidden states can help guide pruning decisions.
Poplesty
Perplesit
Pade th Sar3
Prote de13
Fade Boit Nao?
Plut Baad) 20:
Prone So:
Fade Bost boo
os (a) LLaMA-2-7B, WikiText2
22
12 de as 61
Ca Co ca
@ Se de so
Proze Sequenee Ratio (b) LLaMA-2-7B, ARC-c
Probe Sequence Ratio OPT-13B, WikiTex12
Probe Sequence (d) OPT-13B, ARC-c
Figure 3: Performance of different probe combinations a 40% pruning ratio.
Effect of Probe Combinations Performance. We find that even a small probe can improve model performance. The results shown in Figure 3 We investigate how different probe sizes affect PP's performance by varying the probe batch size from to 20 (specifically, and 20) and the probe sequence ratio from 0.05 1.0 (specifically, 0.05, 0.1, 0.3, 0.5, 0.8, and 1.0). First, we observe that once we apply PP, even a small probe with batch size of and a probe sequence ratio of 0.05 can yield performance improvements. For example, for LLaMA-2-7B, the perplexity drops from 29.8 to 21.7; for OPT-13B, it drops from 35.4 to 27.7. Furthermore, we observe that increasing both the probe batch size and sequence ratio leads to improved performance. Interestingly, we find that the initial increase in sequence ratio from 0.05 to 0.3 brings the most rapid performance improvement. This indicates that sequence information becomes significantly effective for pruning once it exceeds a certain size threshold relative to the current batch's sequence length.
Table 4: Comparison of FLOPs between dense model inference and probing.
Computational Cost (TFL.PS)
Method
WikiTex12
ARC-c
Dense
4420
4377
Probing
66 (1.5%)
69 (1.6%)
Computational Cost and Inference Speed. We use the DeepSpeed pack- age (Rasley al., (020) to measure the FLOPs. The results in Table 4 show that the computational overhead of probing is approximating 1.5% of the FLOPs the dense model inference. This finding aligns with our analyzed computational complex- ity in Section 4.1 Additionally, we evalu- ate each block's 's end-to-end runtime across all batches of WikiText2 and the infer- ence speedup at a 40% pruning ratio on NVIDIA A 100 GPUs, similar to previous studies (Sun et al. 2023 Ma et al. 2023). The results for LLaMA-2-7B on WikiTeex2 are presented 5 We find that the inference speeds of PP are comparable to those of other structured pruning baselines, yet it delivers superior performance. Specifically, in the attention block, PP achieves speedup of 1.46x, and in the MLP block, a speedup of 1.30x. The slight delay observed in the MLP block can be attributed to inherent system costs, such weight extraction. This gap narrows under conditions with larger batch sizes or longer sequence lengths, leading to comparable speeds between PP and the baselines.
Table 5: Breakdown of inference runtime across all batches of WikiText2 at a 40% pruning ratio. The speedup is calculated by dividing the dense model's inference runtime by the methods' inference runtime.
Runtime (S)
Method
PRR
Attention
Speedup
MLP
Specdup
Dense FLAP
0.612
0,416
95.64
0.419
1.46x
0,265
x.57x
Wanda-sp
106.48
0.395
1.55x
0,278
1.50x
PP
37.37
0.420
1.46x
0.319
1.30x
Performance Runtime Ratio. To illustrate the trade-off between model performance and inference speed, we introduce Performance Runtime Ratio (PRR), which quantifies the ratio of performance degradation per unit of runtime reduction. Importantly, smaller PRR value is preferable as it indicates minimal performance degradation per unit of runtime reduction. The metric is defined as:
Perfaense Perfpruned
PRR Runtime Runtimeprund
(16)
where Perfpruned and Runtimeprmeed denote the performance and runtime of the pruned model, re- spectively, and Perfaense and Runtimedanse denote the performance and runtime of the dense model, respectively. As shown in T the PRR of PP is 37.37, indicating a increase of 37.37 in perplexity per second of runtime reduction the attention and ML.P block. In comparison, FLAP and Wanda-sp

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Published as a conference paper at ICLR 2025
have PRR values of 95.65 and 106.48, respectively. PP's PRR values are 2.56x (95.65 compared to 37.37) and 2.85x (106.48 compared to 37.37) more efficient than those of FLAP and Wanda-sp, respectively, indicating significantly lower rate of performance degradation.
Compared with Fine-tuned Baselines. Table 6 compares the performance of PP with fine- tuned baselines LoRAPrune and LLM-Pruner across different pruning ratios for text genera- tion and commonsense reasoning tasks. Without fine-tuning, PP consistently outperforms or closely matches the fine-tuned models. At 20% pruning ratio, PP excels in both tasks across LLaMA-2-7B and L.IMM-2-1388 models. At a 40% pruning ratio, PP achieves compara- ble perplexity and significantly higher reasoning task accuracies. For example, PP achieves 61 on LLaMA-2-13B, while LoRAPrune achieves 53.5 and LLM-Pruner achieves 54.7.
Table Comparison of PP with fine-tuned baselines on LLaMA-2-7B/133 models, with attention and MLP layers pruned: PP consistently outperforms across scenarios without fine-tuning.
Text Generation 1
Commonsense Reasoning f
Method
Pruning Ratio
Fine-tuning
LLaMA-2-7B
LLaMA-2-13B
LLaMA-2-7B
LLaMA-2-13B
Dense
0%
6.0(0.1)
5.1(0.1)
64.0
66.2
LoRAPrune LoRA
20%
8.7(0.2)
7.4(0.0)
59.2
61.0
LLM-Pruner w/ LoRA
20%
10.2(0.3)
8.4(0.5)
58.7
62.1
PP
20%
8.1(0.1)
6.7(0.1)
62.8
65.3
LoRAPrune w/ LoRA
40%
13.6(0.4)
11.1(0.3)
52.1
55.5
LLM-Pruner w/ LoRA
40%
/
20.3(1.3)
15.3(0.7)
50.6
54.7
PP
40%
16.8(0.1)
11.3(0.1)
56.6
61.0
Importance-Scaled Fusion. We compare importance-scales fu- sion three fixed integration ratios- 0.1, 0.5, and 0.9-which assign a fixed ratio to the probing states during integration with his- torical states, We conduct experiments on LLaMA-2-7B using the WikiText22 dataset at 40% pruning ratio, keeping the probe batch size fixed at 1. The results in Figure R demonstrate that importance- scaled fusion can leverage the benefits of the calibration dataset while minimizing associated biases.
Fi Fi
Perplexity
Importazce Stated Faor
02
05
Pruning Metric. Our PPsp consistently outperforms both Wanda- sp and FLAP across various pruning scenarios. We conduct exper- iments on fix-pruned models, each uniquely generated by one of three evaluated metrics, using only the calibration dataset. we evaluated three metrics at uniform 40% pruning ratio across all blocks on the WikiText2 dataset. As shown in Table 7 PPsp significantly reduces perplexity, achieving the lowest scores of 29.7 7 and 35.5 on the LLaMA-2 2-BB and OPT-13B models, respectively, compared to FLAP's 38.2 and 41.1, and Wanda-sp's 43.8 and 42.7.
Probe Sequence Ratio
Figure 4: aepporaance-callee fusion studies.
Table 7: Perplexity WikiText2 across different metrics on models pruned by the calibration dataset, showing that PPsp performs best among the three metrics.
LLaMA-2-7B
OPT-13B
Metric
Formula
Attention
MLP
Attention
MLP
Wanda-sp
Ein [Wirell ||
21.1(
10.9(0.1)
43.8(1.5)
13.2(1.3)
27.5(0.4)
42.7(0.7)
FLAP
N-1
>
(X!!
17.7(0.3)
11.0(0.)
38.2(0.3)
11.6(0.1)
27.3(0.0)
41.1(0.3)
C.m
PPsp
15.4(0.6)
10.9(0.1)
29.7(0.3)
12.9(1.0)
25.1(0.3)
35.5(0.3)
1m0
CONCLUSION
In this paper, we propose Probe Pruning (PP), a novel online dynamic pruning framework that uses a small yet crucial portion of hidden states to run the model and gain crucial pruning information to guide full inference. Notably, PP only relies on the original model structure and hidden states, without requiring additional neural network modules, fine-tuning. Furthermore, PP consistently surpasses all baselines, including those with mtnittnng in almost all experimental settings. Future research directions include refining the probe generation and probing process, integrating PP with advanced decoding and alignment techniques (Wang et al. 2025), and exploring its robustness against poisoned Wang et al. 2024a) adversarial prompts (Xian al. 2025
models (Xian et al. 2023a b
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ACKNOWLEDGMENT
The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Min- nesota for providing resources that contributed to the research results reported in this paper. URL: http:/www.mms http://ww.msi.um..ccom
The work of Qi Le was supported by the Amazon Machine Learning System Fellowship. The work of Xinran Wang and Ali Anwar was supported by the 3M Science and Technology Graduate Fellowship and the Samsung Global Research Outreach Award. The work of Jie Ding was supported part by the National Science Foundation under CAREER Grant No. 2338506 and Grant No. 2220286. The work of Ziyan Wang and Li Yang was supported by National Science Foundation under Grant No. 2348376.
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Appendix for "Probe Pruning"
A IMPLEMENTATION DETAILS
For all methods, we leave the first three layers unchanged, similar Ma et al. (002); Zhang et al. (2023), because pruning parameters in these layers has a substantial impact the model. The pruning ratio represents the average pruning ratio across all attention and MLP blocks in the model. For instance, when targeting pruning ratios of 20% and 40% for LLaMA-2-7B, we prune 22% and 44% from attention and MLP blocks 4 to 32, respectively.
For fair comparison, we utilize the exact subset of the C4 Raffel al. (2220) dataset the calibration dataset.
For PP, FLAP An et al. (024), and Wanda-sp An al. 2024), use 2,000 samples with sequence lengths of I, 1,024 tokens as the calibration dataset for the text generation task, and 2,000 samples with sequence lengths of 512 tokens for the commonsense reasoning task.
For LLM-Pruner Ma et al. (2023). we follow the original implementation details in Ma et al. (2023).
We use 10 randomly selected samples, each truncated a length of 128 tokens, build importance metrics, and 20,000 samples with sequence lengths of 256 tokens for recovery retraining. Specifically, in the recovery stage, we employ the Adam W He et al. (2020) optimizer with 100 warmup steps. set the LoRA Hu et al. (2021) rank r to 8, use learning rate of 1 x 10-4, batch size of 64, and perform recovery retraining for epochs.
For LoR APrune Zhang et al. (2023), we follow the original implementation details in Zhang et al. (2023). We randomly sample 20,000 sentences from the C4 dataset, each having a length of 512 tokens, according the original calibration dataset preparation process. The training hyperparameters include setting the LoRA rank to 8, learning rate x 10 a batch size 128, and a total of training epochs. When fusing pruning with fine-tuning,
employ cubic sparsity scheduler |Sanh et al. (020) to iteratively prune the model until we reach the target sparsity. When only pruning is performed, with tuning conducted match other one-shot pruning methods, samples with sequence lengths of 512 tokens to estimate importance and perform one-shot pruning with no weight updates. All training processes are optimized using the AdamW optimizer with linear learning rate decay.
use 10 selected
B ABLATION STUDIES
investigates how different calibration datasets influence the fix-pruned model, which relies exclusively such calibration dataset. Sec- tion B.2 evaluates the effect of manually squaring the attention metric in the FLAP model et al.) (2024) versus not squaring it. Section B.3 illustrates the batch-dependent outliers token positions. Section B.4 studies the effectiveness of residual importance. Section B.5 studies the integration of historical states and their influence on the performance of Probe Pruning (PP). Section BB6 verifies the possibility of running the probe in parallel with the actual computation of earlier pruned blocks. Section [B7 analyzes the discrepancies between pruning the attention and MLP blocks varying pruning ratios.
In this section, we present various ablation studies. Section B.
B. CALIBRATION DATASET
We present the performance of FLAP An al.] (2024) using different calibration datasets test WikiText2 Perplexity, as shown in Table The results indicate that structured pruning methods, which rely solely on calibration datasets, may introduce biases. For instance, when using the WikiTex12 validation set as calibration dataset, FLAA achieves perplexity of 18 18.5 at 40% pruning ratio on WikiText2. However, with the C4 dataset as the calibration dataset, the perplexity deteriorates to 38,9.
B.2 MANUALLY SQUARING THE ATTENTION METRIC
In the FLAP implementation available at htttps://t www.CASIA- CASIA-SAAAAAAA -VASSAA-V------ LaTAA-La/FLAAA the attention metric is manually squared. Table demonstrates the impact of manually squaring the
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Table 8: Comparison of FLAP performance at different pruning ratios and calibration datasets on LLaMA-2-7B and LLaMA-2-130 models.
Method
Pruning Ratio
Calibration Dataset
LLaMA-2-7B
LLaMA-2-138
20%
C4
10.30(0.1)
7.5(0.1)
20%
WikiText2 validation
7.9(0.1)
6.5(0.1)
FLAP
40%
C4
38.9(1.3)
15.5(0.0)
40%
WikiText2 validation
18.5(0.2)
10.5(0.1)
Table 9: Comparasion of FLAP with and without squaring the attention metric, while keeping the MLP metric consistently unsquared, LLaMA-2-7B and LLaMA-2-13B Models.
LLMA--2-7B
11AMA-2-138
Method
Pruning Ratio
Attention Pruning Ratio
MLP Pruning Ratio
WikiTex22
Attention Pruning Rati
MLP Praning Ratio
Wikffeu22
square
17.8%;(0.1
21.3%/0.1)
10.30.1)
34.7%((..1
18.0((01)
7.5(0.1)
FLAP
205
0.6%(0.))
30.8%(0.1
9.1(0.1)
0.0%3-0.0)
(1.5%(0.))
7.70.1)
FLAP who square
40%
35.456(0.11
42.6%//0.1)
38.9(1.3)
37.5%
41,016(0.1)
15.5000)
40%
17.695(0.3)
52.6%((.11
29.1(0.4)
55.66(003)
attention metric in FLAP versus not squaring it. The findings indicate that squaring the metric results in less aggressive pruning of attention blocks. For instance, with LLaMA-2-7B at 20% overall pruning ratio, the non-squared FLAP method prunes 17.8% of attention weights, in contrast to only 0.6% when squaring implemented. This implies that squaring significantly mitigates attention pruning.
Additionally, less aggressive pruning of attention blocks correlates with better model performance. Specifically, on LLaMA-2-77B at 40% overall pruning ratio, non-squared FLAP prunes 35. 55.4% of attention weights, resulting a WikiText2 perplexity 38.9. Conversely, squared FLAP prunes at reduced rate of 17.6%, achieving lower perplexity of 1. These outcomes suggest that more conservative pruning attention blocks can enhance model performance.
B.3
OUTLIERS AT TOKEN POSITIONS
mack2
Back
5307
Baah-4
-300
Back
4300
Backo
Backo
7
Back
Baak
Beak 10
Baxk
Baxt.1
3000
2000
Back 12
Baxk 12
Baxt
Beak
1000
Baxk
1000
Baxt
Book 16
Book
(a) Layer 10
(b) Layer
Figure 5: Visualization of the 2 norm for each token position of the input hidden states at layers 10 and 20 across the batch and feature dimensions. Experiments are conducted on the LLaMA-2-7B model using the WikiText2 dataset.
In the main text Section we stated that large language models (LLMs) exhibit aatch-dependent outliers, necessitating online dynamic pruning address these dynamic outliers. Figure presents the calculated 2 norms for each token position of the input hidden states at layers 10 and 20 across the batch and feature dimensions. The results demonstrate the presence of batch-dependent outliers
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at each token position, aligning with the observations from existing works Liu et al. (022): Sun et al. (2024).
B.4 RESIDUAL IMPORTANCE
In the main text Section 4.2 we noted that layer normalization significantly alters the input hidden states, thereby preventing their importance from accurately identifying key samples and tokens. To validate this observation, Table I0 compares the effectiveness of identifying key samples and tokens based on residual importance with identification based on the importance of layer-normalized input hidden states (PP without residual importance). The experimental results demonstrate the effectiveness of residual importance.
Table 10: Impact of residual importance on probe generation for LLaMA-2-7B. LLaMA-2-7B.AAppyinn residual importance results in better probe performance.
Pruning
WikiTex2
Beol
PIQA
HellsSwag
WineGrande
ARC=
ARC-e
OBQA
Average
PP wlo residiual insortaanee
2015
10.3(0.))
64.3(0.1)
74.2:0.2)
55.3(0.1)
$3.4/0.5)
52.100.2)
55.6/0.1)
40.2(0.2)
$3.6
20%
8.1(0.1)
69.00.1)
78.1(0.0)
73.5(0.)
66.700.)
42.800.1)
68.5(0.))
40.9/0.2)
6288
PP woo residual importance
40%
37.100.4
62.100.0)
61.1000)
31,000.0
50.2/0.1)
20.400.2)
34,4(0.2)
36.7(0.3)
42.3
PP
40%
16.8(0.1)
62.7002))
74.9/0.1
63.600.0)
$7,5(0.2)
35,500.1)
61.7(0.2)
40.3(0.4)
56.6
B.5 HISTORICAL STATES INTEGRATION
In Table [ the results illustrate how incorporating historical states into the pruning decision process enhances the effectiveness of PP. Specifically, when PP leverages historical states, there is a consistent improvement in performance metrics across all models and pruning ratios compared to scenarios where only probing states are utilized (PP w/o historical states). For instance, at a 40% pruning ratio, using a probe generated from 5% of the batch and 50% of the sequence, PP with historical states reduces the perplexity on WikiTexx2 from 20.1 to 16.9 and improves the average accuracy from 51.2% to 56.6%, compared using only the current probing states without historical data.
Table 11: Performance of integrating historical states under different probe combinations on LLaMA- 2-7B. historical states can enhance PP performance.
Metbod
Probe Generation
Praning Ratio
WikTTes2
BoolQ
PIQA
HellaSwag
Winotinane
ARC :
ARC
OBQA
Average
wol historical states
5% batch, 50%
8.20.3)
68.460.0)
75.800.0
70.40.00
63.2000)
38,9000)
64.400.0)
42.200.0
wis hissorical stutes
10% batch
7.00.1
69.8/0.0)
75.700.0
70.700.)
63.7.0.0
39.303m)
64.90000
41.40.0)
PP wiln hietorical strres
20% botch
70%
7.70.
88 80.30.0)
76 76.7/0.0
70.90000
63800
40.100))
65.4000)
40:320.0
60.9
PP
batch, 50%
20%
$200.1
09.00.11
78.10000
735000
06.70.)
(180011
68:00.0)
40,540.21
batch
8.000.)
67.3/0.1)
77.8:0.3)
73.7/0.0)
64801.1
41.50L1
67.40..)
41.360.3
PP
20% botchs
205
7.00.
68.10.1)
77.500.3)
73.700.)
66 7.03)
42.2/0.1)
68 682200.1
42.700.4)
62.7
PP wis historical atoitos
5% butchh 50%
40%
20 (0.3)
57.40.0)
71.3/0.0
55.2/0.0)
54 6(00)
$1,70.0)
53,3/0.0)
34.600.0)
$1.2
states
17:2004)
62.100.0
72.100.0
56.900.0
58.000)
$4.3000
57.9000)
35.40.0)
PP wol historical states
2015 batch
40%
15.6022)
63.8/0.0)
72.300.0)
$7.6/0.0)
56.50000
33.5/0.0
57.7/0.0)
36.000.0)
53.9
PP
5% beaich, 50%
40%
16.9/111)
62.710.2)
74.9/0.)
63.60.))
57.5
35.5(01)
61.700.2)
40.30.4)
56.6
batch
40%
15.8/0.)
64.3/0.1)
74.500.3)
6422011)
57.9/0.4
37.6/3L)
62.9/0.2)
40.7(1.1)
PP
2015 botchs
40%
(5.100.))
64.7/0.1)
74.3/0.1
64.4/01)
18.1(0.3)
39.0003)
62.5/0.1)
41.3602)
$7.6
B.6 PARALLEL PROBING
We verify the possibility of running the probe in parallel with the actual computation of earlier pruned blocks. We present the results in Table when the actual computation happens on earlier pruned blocks, we generate the probe from the residuals of these earlier pruned blocks and perform the probing. PP represents the default setting used in the main text of the paper. The results show that we can still obtain performance gains and achieve comparable results to PP. For example, at a 40% pruning ratio, PP-Parallel achieves a perplexity of 17.9 on WikiText2, which is close to that of PP and much lower than the 38.9 achieved by FLAP. Furthermore, PP-Parallel achieves 61.4% accuracy on ARC-e, which is close to that of PP and much higher than the 52.5% achieved by FLAP. However, are just demonstrating the feasibility of further improving PP's inference speed here; the actual parallelism is hardware- -epeeddnn and impleeeettation-depeeddn..
Here, PPrParlllel represents the approach where,
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Table 12: Zero-shot performance of LLaMA-2-7B after pruning attention and MLP blocks without fine-tuning.
Method
Pruning Ratio
WikiText2
ARC-e
Dense
09%
6.00.1)
67.3(00)
Full-Batch Probing
20%
7.30.11
67.2(0.0)
Wanda-sp
20%
10,6(0.1)
63.9(0.3)
FLAP
20%
10.3(0.1)
63.1(0.1)
PP-Purallel
20%
8.1(0.1)
67.9(01)
PP
20%
8.1(0.1)
68.5(0.0)
Full-Batch Probing
40%
13.6(0.1)
64.7(0.0)
Wanda-sp
40%
43.8(1.5)
14.400.1)
FLAP
40%
38.9(1.3)
$2.5(0.2)
PP-Parallel
20%
17.9(0.1)
4(0.))
PP
40%
16.8(0.1)
61.7(0.2)
Table 13: Performance of pruning attention heads versus MLPs at different ratios on LLaMA-2-7B, comparing the effects pruning only the attention heads or only the MLPs.
Proning Ratio
"Text Cienenaioon
Commonsense Reasoning
Attention
MI.P
All
WikiTexr2
BoolQ
PIQA
HellaSwag
WinoGirande
ARC-c
ARC-
OBQA
Avemge
0%
0%
6.000.1)
74.600.0)
77.9(0.0)
75(0.0)
67.7(0.0)
42.7(0.0)
67.3(0.0)
42.6(0.0)
64.0
20%
0%
7%
6.8(0.1)
71.10011)
78.6(0.1)
74.7(0.0)
66.3(0.1)
42.9(0.0)
69.00.11
43.1(0.1)
63,7
20%
13%
7.200.1)
68 1(0.))
77.700.0)
74.3(0.0)
67.8(0.))
41,8(0.))
66.9(0.1)
41.3(0.1)
62.6
40%
0%
14%
10.0/0.0)
65.360.1)
77.2(0.1)
69.3(0.1)
58.4(0.2)
38.1(0.1)
64.9/0.0)
40.3(0.1)
59.1
10%
25%
10.1(0.1)
65.910.0)
76.0(0.2)
69.4(0.1)
63.8(0.0)
36.5(0.2)
62.4(0.0)
40.3(0.6)
59.2
60%
0%
21%
33.5(0.4)
60.800.1)
71.4(0.1)
42.2(0.1)
51.8(0.3)
29.9(0.2)
49.8(0.1)
36.4(0.2)
49.0
39%
21.1(0.2)
62.810.1)
71.100.2)
55.30.11
58.600.3
31.4(0.2)
53.4(0.0)
34 8(0.))
40%
20%
27%
11.910.1)
65.060.1)
76.4(0.1)
68.4(0.1)
59.3(0.4)
39.0(0.1)
64,8(0.2)
40.6(0.3)
59.1
20%
40%
33%
11.5(0.1)
67.7(0.1)
75.4(0.3)
69.1(0.0)
62.7(0.2)
38.3(0.1)
64.1(0.2)
41.0(0.4)
59.8
60%
20%
3498
38,440.3)
62 1).000.1)
72.6(0.2)
43,7(0.1)
51.000.2)
29.9(0.1)
52.3(0.2)
38.5(0.4)
50.0
23.8(0.4)
62.5(0.1)
70.8/0.2)
55.6(0.1)
58.5(0.2)
33.2(0.2)
54.6(0.1)
36.2(0.1)
60%
40%
47%
44.300.5
62.0(0.0)
70.8(0.2)
42.8(0.1)
51.000.4
29.0(1.2)
51.2(0.2)
36.9(0.5)
49.1
33.5(1.2)
60.6(0.1)
70.3(0.1)
50.7(0.0)
53.8(0.3)
30.1(0.5)
52.8(0.2)
35.9(0.1)
B.7 DISCREPENCY BETWEEN PRUNING ATTENTION AND MLP.
We find that the pruning ratios for attention and MIP layers should be considered independently. they may reach saturation different points. Table 13 demonstrates clear discrepancy in performance between pruning attention heads and MLPs, especially as the pruning ratios increase. While lower pruning ratios (20%) result in similar performance impacts for both components, higher ratios (40% 60%) suggest that attention heads reach saturation, particularly in demanding tasks such as WikiText2 and HellaSwag. For example, at 60% pruning ratio for attention, performance on WikiTeex2 drops dramatically to 33.5, compared to 21.1 when the MLP is pruned at the same level. Similarly, performance on HellaSwag decreases significantly to 42.2 when pruning attention, compared to 55.3 when pruning the MLP at the same level. Additionally, considering each module's 's actual FLOPs reveals a larger performance gap, emphasizing the need for a strategic approach to pruning neural network components.
C ADDITIONAL EXPERIMENTAL RESULTS
In this section, we present the detailed experimental results for each task. The performance without fine-tuning is shown Tables [5 16, [7d The comparison of PP with fine-tuned baselines is provided Tables 18 and PP consistently surpasses all baselines, including those with fine-tuning in almost all experimental settings.
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Table 14: Zero-shot performance of LLaMA-2-7B after pruning attention and MLP blocks without finennuning: PP demonstrates superior performance in nearly all scenarios.
Method
Pruning Ratio
WikeText2
BoolQ
PIQA
HellaSwng
WinoGmande
ARC-e
ARC-e
OBQA
Average
Dense
0%
6.000.1)
74.6(0.0)
77.9(0.0)
75(0.0)
67.7(0.0)
42.7(0.0)
67.3(0.0)
42.6(0.0)
64.0
Full-Batch
20%
7.3(0.1)
67.9(0.0)
77.0(0.0)
74.5(0.0)
65.9(0.0)
42.7(0.0)
67.2(0.0)
43.2(0.0)
62.6
Wanda-sp sp
10.6(0.1)
65.3(0.1)
77.2(0.1)
74.1(0.0)
67. (7.1()
11.1(0.1)
63.9(0.3)
41.8(0.2)
FLAP
20%
10.3(0.1)
67.3(0.5)
76.6(0.2)
73.0(0.1)
67.4(0.0)
40.6(0.3)
63.1(0.1)
LoRAPrune
20%
22.7(0.9)
64.200.6)
74.600.3)
66.5(0.5)
58.8(1.2)
37.700.7)
63.9(0.6)
39.4(1.1)
57.9
LLM. Pruner
20%
17.5(1.6)
62.5(0.3)
75.3(0.8)
66.0(0.7)
57.2(1.7)
37.7(1.)
62.4(0.7)
40.5(0.2)
57.4
PP
20%
8.1(0.1)
69.0(0.1)
78.1(0.0)
73.5(0.0)
66.7(0.3)
42.8(0.1)
68.5(0.0)
40.9(0.2)
62.8
Full-Batch
40%
13.6(0.1)
64.8(0.0)
74.900.0)
67.6(0.0)
59.0(0.0)
38.7(0.0)
64.7(0.0)
41.0(0.0)
58.7
Wanda-sp
40%
43.8(1.5)
62.5(0.1)
72.5(0.1)
63.3/0.0)
16.9(0.1)
33.4(0.2)
54.4(0.1)
40.8(0.4)
54,8
FLAP
38,9(13)
63.5(0.1)
71.3(0.3)
63.3(0.1)
59.810.1)
33 33.800.6)
52.5(0.2)
40.000.6)
LoRAPrune
40%
129.5(3.0)
54.0(4.2)
65.010.5)
45.1(1.3)
52.1(0.3)
8(0.2)
43.6(0.7)
32.100.6)
LLM-Pruner
40%
51,1(4.3)
55.5(5.0)
69.8(1.1)
49.602.1)
51.2(0.3)
27.8(0.6)
46.0(2.0)
35.0(0.5)
47.8
PP
40%
16.8(0.1)
62.710.3)
74.9(0.1)
63,6/0.0
57.5(0.2)
38.500.1
61.7(0.2)
40.3(0.4)
56.6
Table 15: sero-shot performance of LLaMA-2-13B after pruning attention and MLP blocks without fine-tuning: PP demonstrates superior performance in nearly all scenarios.
Method
Proning Ratio
WikiText2
BoolQ
PIQA
HellaSwag
WinoGrande
ARC-c
ARC-c
OBQA
Average
Dense
0%
5.1(0.1)
72.0(0.0)
79.6(0.0)
78.7(0.0)
70.7(0.0)
46.5(0.0)
71.3(0.)
44,2(0.0)
66.2
Full-Batch
20%
6.2(0.1)
69.0(0.0)
78.7(0.0)
77.9(0.0)
(0.1(0.0)
47.7(0.0)
71.1(0.0)
42.8(0.0)
65.3
Wanda-sp
20%
9,0(0.1)
70.4(1.0
79.4(0.1)
78.4(0.0)
70,2(0.1)
44.3(0.6)
69.9(0.3)
42.5(0.2)
65.0
FLAP
20%
7.5(0.1)
71.1(0.5)
78.7(0.1)
77.3(0.0)
71.2(0.2)
44.6(0.1)
66.7(0.1)
42.5(0.2)
64.6
LoRAPrune
20%
16.100
63.6(0.))
75.4(0.1)
69.4(0.8)
6(0.3)
37.600.4
62.6007)
40.3(0.5)
LLM-Pruner
11.3(0.7)
63.4(1.8)
71.7(0.))
72.3/0.5)
63.0(1.1)
42.3(0.6)
67.8(03)
42.9(0.7)
PP
20%
6.7(0.1)
72.0(0.2)
79.5(0.1)
77.6(0.0)
68.5(0.1)
44.7(0.2)
71.5(0.1)
43.000.)
65.3
Full-Basch
40%
8.9(0.1)
68.4(0.0)
77.7(0.0)
74.5(0.0)
65.4(0.0)
42.4(0.0)
69.3(000
42.8(0.0)
62.9
Wanda-sp
40%
21.600.4)
62.4<0.0)
74.5(0.3)
68.0(0.0)
63.0(0.4)
34,800.5)
54,900.3
38.90044
56.6
FLAP
40%
15.5(0.0)
62.9(0.1)
76.8(0.3)
72.4(0.1)
66.9(0.3)
40.4(04)
63.1(0.4)
41.8(0.1)
60.6
LoRAPrune
40%
74.8(6.4)
57. 53.5)
66.8/0.9)
$1.500.6
53.6(0.5)
28.5(0.3)
46.000.8)
32,4(1.2)
48.1
LLM-Pruner
40%
34.5(2.4)
57. $7.002.2)
72.5(1.1)
57.8(2.0)
54,2(0.8)
33.3(1.3)
51.5(1.9
37.7(1.2)
52.0
11.3(0.1)
65.8(0.1)
77.1(0.2)
71.6(0.0)
61.3(0.)
40.9(0.3)
67.9(0.1)
42.5(0.3)
Table 16: Zero-shot performance of OPT-13B after pruning attention and MLP blocks without fune-nuning: PP demonstrates superior performance in nearly all scenarios.
Method
Pruning Ratio
WikiTex12
BoolQ
PIQA
HellaSwag
WinoGrande
ARC-e
ARC-c
OBQA
Average
Dease
0%
11.6(0.1)
68.1(0.0)
75.3(0.0)
67.9(0.0)
66.8(0.0)
35(0.0)
51.1(0.0)
36.4(0.0)
57.2
Full Batch
20%
12.6(0.1)
63.9(0.0)
75.7(0.0)
67.6(0.0)
67.000.0
34.3(0.0)
50.7(0.0)
35.4(0.0)
56.4
Wanda-sp
17.4(0.))
66.0(0.2)
75.4(0.1)
63.00.1)
64.8(0.3)
33.7(0.0)
48.2(0.2)
35.0(0.1)
FLAP
188(0.2)
68.1(0.4)
75.1(0.1)
62.5(0.2)
62.6(0.3)
31.8(0.3)
49.5(0.1)
34.5(0.1)
20%
14.7(0.1)
67.4(0.1)
75.5(0.1)
65.7(0.0)
64.9003)
33.8(0.1)
51.6(0.0)
36.5(0.2)
56.5
Foll-Batch
40%
17.9(0.2)
52.1(0.0)
75.7(0.0)
64 (4.8(0.0
65.5(0.0)
32.8(0.0)
50.100.0)
36.800.0)
Wanda-sp
40%
42.7(0.7)
(3.7(0.1)
71.8(0.3)
53,2(0.1)
57.6(0.2)
29.6(0.4)
43.3(0.1)
(4.2)
50.5
FLAP
40%
51.0(0.7)
62.7(0.0)
72.4(0.0)
53,3(0.2)
58.3(0.5)
29.4(0.3)
45.2(0.4)
34.1(0.))
50.8
PP
40%
26.7(0.3)
61.1(0.2)
74.3(0.1)
58.7(0.0)
$9.3(0.1)
33.6(0.1)
49.7(0.1)
35.3(0.4)
53.1
Table 17: Zero-shot performance of pruning LLaMA-3-8B with MLP pruned. PP consistently demonstrates superior performance across nearly all tested scenarios.
Method
Pruning Ratio
WikiTou12
BoolQ
PIQA
HellaSwag
WinoGrande
ARC-c
ARC-c
OBQA
Average
Dense
0%
6.8(0.0)
81.7(0.0)
79.5(0.0)
76.3(0.0)
72.5(0.0)
47.2(0.0)
61.7(0.0)
40.2(0.0)
65.6
Fall-Barch
20%
8.5(0.0)
79.0(0.0)
80.1(0.0)
74.8(0.0)
73.900.0)
44.9(0.0) ))
60.7(0.0)
40.2(0.0)
64.8
Wanda-sp
20%
10.000.0)
75. (00.))
78.5(0.0)
69.600.2)
71.40..4
38.70..4)
56.9(0.4)
39.0(0.2)
61.3
FLAP
20%
10.0(0.0)
79.4(0.2)
78.7(0.1)
70.3(0.0)
71.4(0.5)
40.8/0.1)
57.8(0.0)
39.4(0.3)
62.5
PP
20%
9,3(0.0)
77.4(0.0)
78.5(0.0)
73.1(0.0)
72.5(0.3)
43.2(0.3)
59.1(0.2)
40.2/0.5)
63.4
Full- Batch
40%
12.3(0.1)
73.100.0)
77.8/0.0)
70.5(0.0)
70.3(0.0)
42.9(0.0)
58.9(0.0)
39.8(0.0)
61.9
Wanda-sp
40%
18.4(0.1)
66.6(0.1)
73.4(0.2)
56.7(0.1)
63, (0.2()
31.8/0.2)
47.0(0.5)
34,5(0.2)
53.3
FLAP
40%
18.5(0.2)
67. (1.3((1))
73.5(0.0)
57.2(0.2)
66.7(0.5)
31.7(0.3)
44.6(0.3)
34.4(0.3)
53.6
40%
14.9(0.1)
70.3(0.1)
76.3(0.2)
68.3(0.1)
67.2(0.2)
39.000.3)
57.410.1)
36.9(0.3)
19

---

Published as a conference paper at ICLR 2025
Table 18: Comparison of PP with fine-tuned baselines on LLaMA-2-7B model, with attention and MLP layers pruned: PP consistently outperforms across scenarios without fine-tuning,
Method
Pruning Ratio
Fine tuming
WikeTeaz2
DoolQ
PIQA
HellaSwag
WinoGrande
ARC
ARC
ODQ4
Average
74.600.))
7500.)
67.70.00
42.700.1)
LoRAPrune wi LoRA
20%
8.7022)
67.010.9)
76.500.2
69.10.13
63.20.3)
36,7002)
58.900.9)
42.3003)
59.2
L.M-Prmerr LeRA
20%
10.200.3)
66.6113)
76.10.6)
68.40.5)
62.8011)
36 36.30.4)
80.8003)
40.7(0.7)
SK.7
8.1(0.))
69.00011
78.100.0)
73.500.0)
66.700.3)
42.80.11
68.500.0
40.9/023
LoRAPrune LoRA
40%
13.40.4)
62.966.2)
70.80.1)
58.600.13
35.500.7)
30.900.4
89.600.4
26.700.)
52.1
$3.10.5)
PP
40%
16.00.11
62.700.2)
74.90011
63.6/0.0)
57.5(0.2)
35.30011
61.700.2)
40.30044
56.6
Table 19: Comparison of PP with fine-tuned baselines on LLaMA-2-13B model, with attention and MI.P layers pruned: PP consistently outperforms across scenarios without fine-tuning.
Pruning Radoo
suing
BoolQ
PIQA
HelluSwag
OBQA
Average
Derist
0%
5.100.11
10.00
70.60000
78. 78.700.0)
70.7(0.0)
46 46.500.0)
71.30-00
44 44.2/0.0)
56.2
74.8/0.2
66.00033
LLMM-Pruner w/ LoRA
2096
8.4/0.5)
70.211.4)
75.300.3
13.8/033
65.8(1.3)
20.100.5
64.2(0.4)
42.000.4)
62.1
PP
6.710.11
72,000.31
79.500.1
77.6/0.)
68.500.1)
65.3
LRRAPrrne LoRA
11.10.3)
63.50011
74.100:)
65.5/0.1)
(0.4/0.)
33.00.4)
53.90077)
10.300.6
LLM- LoRA
40%
15.500.7)
63 900.)
73.5/2.0)
62.4(114)
57.5(1.3)
33 33.2(1.2)
55.2(0.7)
37.500.8)
54.7
11.3003)
77.1(0.2)
71.6/0.8
61.3/0.)
40.9(0.3)
67.91..1
42.5(0.3)
20


Reviews:
[{"review_id": "OhQCC1M5gu", "reviewer": "Reviewer_tnLv", "summary": "This paper tackles an important problem and explores saliency-aware structural pruning of neural networks. It utilizes a probing module that analyzes the L2-norm based importance for network slimming and delivers run-time inference speedup. It studies Llama2/3 and OPT LLMs and translates into performance improvements.", "questions": "Listed as above in the weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method seems to be easy to implement without incurring extensive hardware changes.\n- Various ablations are provided.\n- Real-time speed ups are observed on GPUs, that are a critical aspect for deployment.", "weaknesses": "- The method seems not to be zero-shot as it does rely on a calibration set. I could not find the detailed calibration datasets descriptions in Section 5, making it likely that the probing is looking into the final datasets to find optimal and satisfactory combinations. This makes is fairly unfair to other methods that are zero-shot on final tests given other proxy datasets. I would like to have more clarifications whether probing is looking into the final test/target metric data for numbers. If so, it's not a fair comparison to others and calibration set used for probing shall be used kept the same as other methods and report only zero-shot test values.\n- Figure 3, zero probe sequence ratios all setups are below the reference line - why is it the case?\n- Overall I think the method is gradient-free, but does not seem to be completely test-time per-sentence dynamic. Rather it is using batch and sequence data for a quick analysis of the saliency and deploy pruning methods. \n- Table 5 indicates the method is not giving final speedups and performance boosts as strong as FLAP and Wanda-SP. This raises slight concerns for the true strength of the methods.\n- Probing can incur overheads due to look aheads, and that may imply why the speedup is limited to <2x. It would be beneficial to see higher speedup ratios that may make the methods more attractive.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Vwbe7Re5v0", "reviewer": "Reviewer_ebT5", "summary": "This paper presents a  framework for online dynamic structured pruning of Large Language Models (LLMs). The proposed method, called Probe Pruning (PP), utilizes a small subset of hidden states to identify crucial weights, enabling tailored pruning for different batches during inference. Key components of the framework include probing, which gathers important intermediate hidden state information, and history-informed pruning, which integrates probing states with historical data to enhance pruning decisions. The results demonstrate that PP significantly improves efficiency and performance compared to existing methods, achieving better outcomes without the need for fine-tuning or additional neural network modules. The paper highlights the potential of PP for optimizing LLMs while maintaining their effectiveness.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper demonstrates that PP can achieve substantial reductions in computational overhead while maintaining or even improving model performance. The ability to perform dynamic pruning during inference allows for tailored optimizations that adapt to the specific characteristics of each batch, enhancing overall efficiency.\n\n\nThe authors provide comprehensive experimental results across various tasks and models, including LLaMA-2 and OPT. The consistent outperformance of PP compared to existing baselines, including those that involve fine-tuning, underscores the effectiveness and reliability of the proposed method.", "weaknesses": "1. Unfair Comparison: This approach is a dynamic inference technique, but it is mainly compared to some static pruning techniques. The static pruning work is generally less accurate than dynamic pruning, but it reduces the LLM storage overhead and accelerates the results consistently. Dynamic pruning cannot reduce the LLM storage overhead and the acceleration result varies greatly under different inputs.\n\n2. Lack of comparison of other dynamic inference approaches. For example, some dynamic early exits can be seen as dynamic depth pruning, but also other token-level dynamic decoding methods. Authors should present comprehensive comparisons and discussions, and clearly explain the advantages of dynamic weight pruning over other dynamic inference methods.\n\n3. Not enough impressive novelty: The Historical States method is common operation similar to  EMA or Memory Bank in contrast learning. I consider this technique to be a trade-off strategy rather than an essential solution.\n\n\n4. Absence of recent LLM evaluations: this work mainly evaluates LLMs released in 2023, the latest LLMs released in 2024 are lacking in experiments. e.g., Llama 3 70B,  Llama 3.2 1B and 3B, DeepSeekMoE，Mixtral-8x7B.\n\n5. Lacking some discussion of recent LLM pruning efforts, such as Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models  (ICML-2024)  Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models (NeurIPS-2024) Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment  (NeurIPS-2024)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nSuE8RrWe5", "reviewer": "Reviewer_NzRF", "summary": "The paper proposes an approach for on the fly pruning of LLM. On the fly means that the structure is changing depending on the prompt. The approach is motivated by observation that using different data for calibration results in different pruning structures. To make the approach more efficient, only a portion of samples and tokens is selected for importance estimation. Results are solid and show a trade-off between improvements in accuracy, but also a slowdown in throughput.", "questions": "Statements as on page 1 need empirical evidence to backup the statement: \"Although the calibration dataset provides valuable insights for pruning by identifying non-critical weights, this approach overlooks the batch-dependent nature of outlier properties in LLMs Liu et al. (2023b); Song et al. (2023); Liu et al. (2024); Sun et al.\"\n\nPaper talks about \"residual importance\" a lot, but this concept is not clear before page 5. It would be better to introduce it earlier. Does it mean an L2 norm of the hidden state? If yes, then it will be better to simplify description. \n\nHow to understand \"Full-Batch Probing\" - is it just a standard structured pruning? How is that different from other techniques? \n\nFrom tables 2-3 it seems that full-batch probing outperforms all other techniques, is it because pruning mask changes between batches? \n\nWhat is pruned exactly? Are heads pruned, in intermediate dimension in MLP pruned, what about hidden dimension and layers?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "- The problem is interesting and novel.\n- The approach is well motivated and look novel. \n- The motivation behind selective importance estimation is great.\n- I like the way the importance is tracked and updated from initial states. \n- Results are solid, they show clear benefits of the pruning \"on the fly\"", "weaknesses": "- The improvement in perplexity comes with reduction in model throughput as shown in Table 5.\n- The method has weaknesses when compared to other techniques. For example the entire model needs to be load, whereas for other structured pruning techniques, only the pruned version is required. Limitations and weaknesses are not highlighted in Table 1 forming an incomplete story. \n- The paper is written in a complicated way, it feels that the idea and method can be described in a more intuitive and simpler way. I don't count this as a strong weakness, but rather encourage authors to improve the paper for future readers. \n- For evaluations, is it implemented as the entire batch consists of a single task? What happened when the batch is mixed? I would assume degradation of the current approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "39RCvRHbq6", "reviewer": "Reviewer_vvRG", "summary": "The paper acknowledges challenges incurred by the majority of pruning methods:\n* That relying on a calibration dataset to collect pruning statistics introduces potential bias, in that the calibration dataset cannot be perfectly representative of real-world data.\n* That pruning requires access to information, such as gradients, that are not usually available during inference, precluding dynamic pruning.\n\nThe paper hints at the fact that dynamic pruning is poised to surpass dynamic pruning in performance and proposes a framework to make it practically feasible.\n\nIn the paper, the authors recognize that structured pruning is, according to most implementations, the only feasible way to effectively speed up inference.\n\nThe paper introduces probing, as a means for collecting statistics required for pruning at negligible additional cost.\n\nThe paper provides a configuration named Full-Batch Pruning that gives an upper-bound limit to the accuracy of dynamic pruning. This can be used to better measure the effect of sampling during probing. The paper shows that the selection of prunable weights is more similar to that of Full-Batch Pruning for PP than it is with the fixed-prune models.\n\nHistory-informed pruning is introduced the alleviate the inaccuracy of probing when the sampling rate is small, by accumulating statistics over multiple batches.\n\nA pruning metric is introduced. The metric is based on the Wanda metric, with an additional squaring to capture the importance of individual weights.\n\nThe paper concludes with a large amount of empirical results and detailed ablations in the appendix.", "questions": "1. Will history-informed pruning counterbalance the limited sampling possibilities when the batch size of sequence length are small?\n\n2. Can you run end-to-end inference measurements with PP in order to show how much speed up we can expect from 20% and 40% pruning ratios?\n\n3. Does PP speed up the auto-regressive phase of inference?\n\n4. Can you compare against the Minitron baseline?\n\n5. Can you further explain why the proposed pruning metric is superior to the Wanda metric?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The challenges described in the introduction are indeed fundamental limitations of most existing pruning methods.\nDynamic Pruning is a very promising research avenue and this paper brings several novel aspects to it: probing, pruning metric.\n\nThe paper provides a good set of baselines to compare against.\n\nThe paper validates the method with several model backbones (LLaMA2, LLaMA3.1, OPT) and benchmarks that include text generation and common reasoning tasks.\n\nEvery design choice in the paper is carefully described, ablated and proven with experimental results.", "weaknesses": "The method relies on having rather large batch sizes and sequence lengths to sample from in order to generate a probe. However in many inference setups where speed matters and thus pruning is desirable, the batch size is small, sometimes down to 1. Similarly, inference time in LLMs tends to be dominated by the auto-regressive (aka generation) phase, where tokens are produced one at a time. \n\nThe inference measurements and FLOPs calculations demonstrate the small computational complexity of running the probe. However in practice, I understand that we need to run the probe first and only then do we know which filters to prune. Potentially, the probes can run in parallel with the actual computation of earlier pruned blocks. In order to prove the point that the benefits of probing + dynamic pruning outweigh the cost of probing, it would be nice to have end-to-end inference measurements.\n\nThe paper does not compare against the Minitron baseline (https://arxiv.org/pdf/2408.11796), yet this paper shows better metrics. For example for LLaMA3.1-4B, LLaMA3.1-Minitron4B(50% pruning) has HellaSwag at 76.1, while PP has it at 65.3 with 40% pruning. Thus it is unclear whether the method beats the State of the Art.\n\nThere is empirical data to probe the superiority of the proposed pruning metric but no theoretical justification.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)