From 6578c8e96712c3105af380a4ce138e23558e3bc8 Mon Sep 17 00:00:00 2001 From: Luka Ribar Date: Fri, 27 Sep 2024 13:43:26 +0100 Subject: [PATCH 01/11] Add September placeholders --- .../2024-09/2024-09-27-title-tbd.md | 25 +++++++ .../2024-09-27-paper_summary_template.md | 73 +++++++++++++++++++ 2 files changed, 98 insertions(+) create mode 100644 _posts/papers-of-the-month/2024-09/2024-09-27-title-tbd.md create mode 100644 _posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md diff --git a/_posts/papers-of-the-month/2024-09/2024-09-27-title-tbd.md b/_posts/papers-of-the-month/2024-09/2024-09-27-title-tbd.md new file mode 100644 index 0000000..3e14f1f --- /dev/null +++ b/_posts/papers-of-the-month/2024-09/2024-09-27-title-tbd.md @@ -0,0 +1,25 @@ +--- +title: "September Papers: Title TBD" +header: + teaser: /assets/images/posts/2024-08/potm/twitter_card.png + image: /assets/images/posts/2024-08/potm/twitter_card.png + og_image: /assets/images/posts/2024-08/potm/twitter_card.png + +date: 2024-09-27T01:00:00-00:00 +potm_year: 2024 +potm_month: 9 + +layout: paper-summaries-layout +category: "papers-of-the-month" +toc: true +toc_sticky: true +toc_label: "Papers" +toc_icon: "book" +author.twitter: "GCResearchTeam" +--- + +TODO: blurb + +--- + +{% include paper-summaries.md %} diff --git a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md new file mode 100644 index 0000000..423635a --- /dev/null +++ b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md @@ -0,0 +1,73 @@ +--- +title: "" +paper_authors: "3 first authors)>" +orgs: "" +paper_link: "https://arxiv.org/abs/<...>" +tags: + - efficient-inference + - quantisation # Use https://graphcore-research.github.io/tags/ as reference +potm_year: 2024 +potm_month: 9 +paper_order: 1 # Editor will decide +image_dir: "/assets/images/posts/2024-09/potm//" +review_author: + name: "" + link: "" +hidden: true +--- + +[200 words is a rough guide for the length of a summary. +Feel free to go a fair bit over or under if needs be. +The editor will fix any issues with images being rendered too wide/narrow etc. +See README for how to view locally if you wish to (not required. Contact CB if this +is broken for you.)] + +### The key idea + +A few sentences outlining why the paper is interesting... + +Add images where appropriate throughout. This section should always +have at least 1 key figure though. + +*Please use high-res images (zoom in for those screenshots!)* + +A specific and succinct sentence or two describing the figure (alt text). Valuable for seo and accessibility. +
Figure 1a. If the caption isn't included in the image, it should be added like so.
+ +### [optional] Background + +If necessary, a short intro to background matierial needed to understand the method + +### Their method + +Latex can be included in the standard way, either inline: $R=\sum _{t=0}^{\infty }\gamma ^{t}r_{t}$ + +Or as a block: + +
+$$ +Q_{t+1}^{A}(s_{t},a_{t})=Q_{t}^{A}(s_{t},a_{t})+\alpha _{t}(s_{t},a_{t})\left(r_{t}+\gamma Q_{t}^{B}\left(s_{t+1},\mathop {\operatorname {arg~max} } _{a}Q_{t}^{A}(s_{t+1},a)\right)-Q_{t}^{A}(s_{t},a_{t})\right). +$$ +
+ +Code can also be included in the standard way: + +``` +import popart + +builder = popart.Builder() + +# Build a simple graph +i1 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1, 2, 32, 32])) +i2 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1, 2, 32, 32])) + +o = builder.aiOnnx.add([i1, i2]) +``` + +### Results + +... + +### [optional] Takeaways + +... From ec344751b90370bda9d2a70fdc3cc79e1fa41f6c Mon Sep 17 00:00:00 2001 From: Charlie Blake Date: Fri, 27 Sep 2024 15:02:23 +0100 Subject: [PATCH 02/11] add llm correction summary --- .../2024-09-27-llm-correction-via-rl.md | 98 ++++++++++++++++++ .../2024-09-27-paper_summary_template.md | 73 ------------- .../potm/llm-correction-via-rl/figure-6.png | Bin 0 -> 92134 bytes .../potm/llm-correction-via-rl/table-1.png | Bin 0 -> 111260 bytes .../potm/llm-correction-via-rl/table-4.png | Bin 0 -> 128732 bytes 5 files changed, 98 insertions(+), 73 deletions(-) create mode 100644 _posts/papers-of-the-month/2024-09/papers/2024-09-27-llm-correction-via-rl.md delete mode 100644 _posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md create mode 100644 assets/images/posts/2024-10/potm/llm-correction-via-rl/figure-6.png create mode 100644 assets/images/posts/2024-10/potm/llm-correction-via-rl/table-1.png create mode 100644 assets/images/posts/2024-10/potm/llm-correction-via-rl/table-4.png diff --git a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-llm-correction-via-rl.md b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-llm-correction-via-rl.md new file mode 100644 index 0000000..b9e2874 --- /dev/null +++ b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-llm-correction-via-rl.md @@ -0,0 +1,98 @@ +--- +title: "Training Language Models to Self-Correct via Reinforcement Learning" +paper_authors: "Kumar, Zhuang et al." +orgs: "Google DeepMind" +paper_link: "https://arxiv.org/abs/2409.12917" +tags: + - self-correction + - reinforcement-learning + - fine-tuning + - LLMs +potm_year: 2024 +potm_month: 9 +paper_order: 1 # Editor will decide +image_dir: "/assets/images/posts/2024-10/potm/llm-correction-via-rl/" +review_author: + name: "Charlie Blake" + link: "https://x.com/thecharlieblake" +hidden: true +--- + +### The key idea + +Users of LLMs will be aware that sometimes they can recognise and correct their own mistakes. This prompts the question: if the model has the capability to identify some of its own failures, can we leverage this to improve the model? + +This is easier said than done. This paper shows that supervised fine-tuning (SFT) --- the dominant post-training approach for LLMs --- has some inevitable failure modes when trying to teach a model to self-correct. What's needed, and what they demonstrate, is that an RL-based approach can prevail. + +This is significant: _true RL_ has only just broken into the LLM training space, in the form of [OpenAI's o1 model](https://openai.com/index/learning-to-reason-with-llms/), but few details have been released. This work presents a significant step towards realising the benefits of RL in helping language models to reason better. + +An overview of the method, named SCoRe. Supervised approaches can lead to distributional mismatch or never-correcting behaviours. SCoRe addresses this via a 2-stage RL process, where stage 1 encourages the model to produce effective corrections and stage 2 focuses on both initial response and correction. +
An overview of the method, named SCoRe. Supervised approaches can lead to distributional mismatch or never-correcting behaviours. SCoRe addresses this via a 2-stage RL process, where stage 1 encourages the model to produce effective corrections and stage 2 focuses on both initial response and correction.
+ +### Background + +The most straightforward approach to solving the self-correction problem is simply: + +1. Take a dataset of question-answer pairs for some reasoning task +2. For each, prompt the model to generate a solution +3. Evaluate each and remove those solutions which are _correct_ +3. Then prompt the model to generate a correction to the incorrect solution +4. Evaluate the final solutions, and now filter out the _incorrect_ ones +5. Take this dataset of 2-stage "corrected" answers and train the model on it + +This is the basis of the [STaR method](https://arxiv.org/abs/2203.14465), which the authors use as a baseline, alongside PairSFT, which works similarly, but uses arbitrary pairs of incorrect-correct responses to a given prompt as training data. + +The authors test these methods and see the following: + +An evaluation of the STaR and PairSFT baselines, showing that neither is able to offer significant improvements. + +STaR slightly improves the initial attempt, but is poor at correcting --- so much so that it tends to make answers worse, not better! Pair-SFT offers a modest accuracy improvement, though this is largely down to a drop in the value of the final column, which indicates the fraction of correct responses the model ruins via wrong "corrections". So in summary: the only improvement we really see is the model learning to be much more cautious in correcting itself. + +They trace these difficulties down to two problems: + +1. The model tends towards a **minimal edit** policy, where it tries to change as little as possible to avoid degrading the original response. +2. The model is trained on data from its original distribution over responses, yet training causes this distribution to change, leading to **distribution mismatch**. + +### Their method + +The two-stage RL-based method they design aims to target the problems outlined in turn. + +**Stage 1:** The first stage uses RL to maximise the following objective: + +
+$$ +\mathcal{E}\left[ \hat{r}(\mathbf{y}_2, \mathbf{y}^*) - \alpha D_{KL} \left( \pi_{\theta}(\cdot \| \mathbf{x}_1) \| \pi_{\text{ref}}(\cdot | \mathbf{x}_1) \right) \right], +$$ +
+ +Here $\hat{r} (\mathbf{y_2}, \mathbf{y^*})$ is some "correctness" function that acts as a reward, which crucially is based on $\mathbf{y_2}$, the model's _second_ attempt at the problem. The KL term acts on the _first_ attempt, encouraging the model to keep it's first guess that same as the original ("reference") model. + +We can see from this that the aim is to encourage the model to learn strong correction behaviour, by fixing the first attempt and optimizing just the second (approximately). This addresses the minimal edit problem. + +**Stage 2:** Having encouraged strong correction in stage 1, the full problem is addressed in stage 2, which maximises: + +
+$$ +\mathcal{E}\left[ \sum_{i=1}^{2} \hat{r}(\mathbf{y}_i, \mathbf{y}^*) - \beta D_{KL} \left( \pi_{\theta}(\cdot | \mathbf{x}_i) \| \pi_{\text{ref}}(\cdot | \mathbf{x}_i) \right) \right] +$$ +
+ +Here the RL objective is over both attempts, with a weaker KL penalty over both acting as a mild regulariser. A reward-shaping step is also used here to up-weight examples where incorrect first attempts are successfully corrected. + +The key difference between this and SFT is that the data used to update the model is always generated by the current model. This avoids the distribution mismatch problem. + +### Results + +In short, it works. Results are good on maths problems, and even better on coding tasks: + +An evaluation of the base model, Self-refine and PairSFT versus SCoRe on the HumanEval coding benchmark. SCoRe is much better at correcting itself than other methods. + +The first-attempt accuracy is slightly degraded, but the second attempt is substantially better than any other attempt by other methods. The main reason for this is shown in the second-to-last column: a large increase in incorrect answers becoming correct, which is the key objective. + +The paper shows a number of other evaluations and ablations, making a strong case for the method. + +### Takeaways + +This paper makes a compelling case for why supervised fine-tuning is limited as a post-training procedure, and for some problems (such as self-correction), some kind of on-policy RL is required. Carefully designed objectives are required to make this work, but it appears to significantly boost a model's ability to reason at inference time. + +This is just the start. The authors consider a fairly simple problem setting: a single correction attempt on a zero-shot answer, with no supervision as to the source of error. One could imagine a similar approach taken using many correction attempts, possibly on chain-of-thought responses, and with more granular feedback. This promises to be a significant direction of future LLM research, with significant computational and algorithmic implications. diff --git a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md deleted file mode 100644 index 423635a..0000000 --- a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-paper_summary_template.md +++ /dev/null @@ -1,73 +0,0 @@ ---- -title: "" -paper_authors: "3 first authors)>" -orgs: "" -paper_link: "https://arxiv.org/abs/<...>" -tags: - - efficient-inference - - quantisation # Use https://graphcore-research.github.io/tags/ as reference -potm_year: 2024 -potm_month: 9 -paper_order: 1 # Editor will decide -image_dir: "/assets/images/posts/2024-09/potm//" -review_author: - name: "" - link: "" -hidden: true ---- - -[200 words is a rough guide for the length of a summary. -Feel free to go a fair bit over or under if needs be. -The editor will fix any issues with images being rendered too wide/narrow etc. -See README for how to view locally if you wish to (not required. Contact CB if this -is broken for you.)] - -### The key idea - -A few sentences outlining why the paper is interesting... - -Add images where appropriate throughout. This section should always -have at least 1 key figure though. - -*Please use high-res images (zoom in for those screenshots!)* - -A specific and succinct sentence or two describing the figure (alt text). Valuable for seo and accessibility. -
Figure 1a. If the caption isn't included in the image, it should be added like so.
- -### [optional] Background - -If necessary, a short intro to background matierial needed to understand the method - -### Their method - -Latex can be included in the standard way, either inline: $R=\sum _{t=0}^{\infty }\gamma ^{t}r_{t}$ - -Or as a block: - -
-$$ -Q_{t+1}^{A}(s_{t},a_{t})=Q_{t}^{A}(s_{t},a_{t})+\alpha _{t}(s_{t},a_{t})\left(r_{t}+\gamma Q_{t}^{B}\left(s_{t+1},\mathop {\operatorname {arg~max} } _{a}Q_{t}^{A}(s_{t+1},a)\right)-Q_{t}^{A}(s_{t},a_{t})\right). -$$ -
- -Code can also be included in the standard way: - -``` -import popart - -builder = popart.Builder() - -# Build a simple graph -i1 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1, 2, 32, 32])) -i2 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1, 2, 32, 32])) - -o = builder.aiOnnx.add([i1, i2]) -``` - -### Results - -... - -### [optional] Takeaways - -... diff --git a/assets/images/posts/2024-10/potm/llm-correction-via-rl/figure-6.png b/assets/images/posts/2024-10/potm/llm-correction-via-rl/figure-6.png new file mode 100644 index 0000000000000000000000000000000000000000..dd4282cfdabbf6a90e48219e8863264121219a4d GIT binary patch literal 92134 zcmeEuV|XRowrJF`)3L3NZCf2D9ouGyoup&iwr$(CZQIFPefHkxp7-wk|Nh+deYI9q z%{k`qtTD!%wSvFPioro+K?4B+!AXb#ka&4h%$O9%-O ze7CnTHnTJW0-}g^isBI$1peyJHyR{d#ZOe!ot@oHq%CNj4X?nQgsQZ9`;7j4>x#9# zfOsxgPQwZYCm=)vH$8&|hAJ|(gvYM*&>}}&f7G+@g>%893Gc|RtTnmhV_9J;+$%6S za}!ednv4f-M;{A|EHh0sKD$#*A@BB)OV6#4Qy)wA@-eBK{ z@-3}zsVCWnCYmic-zVZmE9h>o(;9o 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Since Adafactor is a rank=1 variant of Adam, the proposed method SOAP runs Adam in Shampoo's eigenspace instead. + +SOAP performance versus Adam and Shampoo, showing good step-efficiency (due to Adam) and time-efficiency (due to periodic preconditioning). Less frequent preconditioning hurts Shampoo more than SOAP. + +### Background + +[Shampoo](https://arxiv.org/abs/1802.09568) for matrices looks like this: + +

+ +Where $W \in \Re^{m \times n}$ is a weight matrix, $L\in \Re^{m \times m}$, $R\in \Re^{n \times n}$ are "preconditioners", behaving a bit like optimizer state and $G$ is the minibatch gradient. + +A slightly different variant is considered here: idealized Shampoo with power $1/2$, + +
$$\begin{aligned} +L &= \mathbb{E}(G G^{\top}) \\ +R &= \mathbb{E}(G^{\top} G) \\ +W_t &= W_{t-1} - \eta \cdot L^{-1/2} G_t R^{-1/2} \,/\, \mathrm{tr}(L) +\end{aligned}$$
+ +The authors show that the last line is equivalent to idealized Adafactor in the _Shampoo eigenspace_: + +
$$\begin{aligned} +Q_L &= \mathrm{Eigenvectors}(L) \\ +Q_R &= \mathrm{Eigenvectors}(R) \\ +G^{\prime} &= Q_L^{\top} G Q_R \\ +W_t &= W_{t-1} - \eta \cdot Q_L^{\top} \mathrm{Adafactor}(G^{\prime}) Q_R +\end{aligned}$$
+ +### Their method + +Based on this link between Shampoo and Adafactor, the authors propose SOAP, which runs full Adam in the Shampoo eigenspace and increases efficiency by only updating the eigenvectors periodically (e.g. every 10 steps). + +SOAP algorithm panel, running Adam in the Shampoo eigenspace. + +The running state of this technique includes $L$, $R$, $Q_L$, $Q_R$, $M$ (in the weight space) and $V$ (in the Shampoo eigenspace). For large projections, such as the final projection layer in an LLM, the corresponding $Q_L$ or $Q_R$ can be fixed to identity. If both are fixed, SOAP reproduces Adam. + +### Results + +Results on language modelling (see figure above) show good step-efficiency of SOAP since it is based on Adam rather than Adafactor, and time-efficiency since the eigenvectors can be periodically updated without substantially harming performance. Like Shampoo, the extra optimization cost can be reduced by using a large batch size. + +Stepping back for a moment, I'm excited about this progress using Shampoo variants and am eager to see experiments over long training runs of LLMs. Hopefully, then, we'll see plenty more shower-related puns on arXiv over the next year! diff --git a/assets/images/posts/2024-09/potm/soap/algorithm.png b/assets/images/posts/2024-09/potm/soap/algorithm.png new file mode 100644 index 0000000000000000000000000000000000000000..c589999f4e9d4603ea7441d5bbcc2794071d6503 GIT binary patch literal 676058 zcmeFZWmJ`I_b-Z|fPi#KNTY;Ei!?|{NOws|gCHP{bc2YLlytYWbcZ5c(juKwl4q{} z82gNI_ITg(aevx;c^-AK)_q_1bBL z{$?79i4Fd9`>ps3HFWs#L^lnAKi_wf)N)d_f9>?n$k7bR+|J(C?D1O@M>8|Kw-)wJ zdnhf!@S?|v7d>}0Gjg)Dx1(0Gv^7JLaEy0N^xk+uKyE%w^_;C8C{ z?(Te2)7x|be3}3xy#M^8e3xeY9g@U<`H>9mBm6J_q?XB^byIPd)DPs59QB%e$DpFb@VKk)zF-2c9$|G!Vp zIN$OXey9Yw!+&|Ucm?zUKXtS{|Ia>+=L(o_-o2ZYqWCX6M?zAZHURe=`@e10{Ct^X z%Kv&il#Tz--p&6#IRE=;{jXKw^MH(Gq5M zf>iv=dmHcvw@$h~f4Kdan6)ESh+z1e@g43IjLGWR~?T2!ky zRiP0iYf1zLr5g>=)f3Bk-~5-d$4%!EXns)eK}gVd4{g?De8crI;pd>)#)p{;64H8d z^4;NDwx3=|90`9|8n1HA?tie{dFpS}vdR5*q;P&dGW3eE{XmEIqhTklu)0OY*lB_U zy7IOaH5(iD!B;cqlE1IWCxRuj?ODsl0nqU zia9eYD<&~9GSGbua5P3rkL*oQS(MUfwW z6%r4ahOG`Hm>ev3vGekF`=j9~<;W7H?%EH;Kkn`ARr2s)VPQGiozo4*i>a}hD!(<5 z@T8~8e28$MP`xOKh(+VoNcIa!vT!}NoQY zm6d3bAGKc1-dFtJnwmHqmOGKD%so8?HDv71K-mGL(369tgH( zJP&@e{*&hNY$bYqtRS}eIW6;n-CV;RdwcuackV1LcZIuNo%3X?FfnVEVv&=RPtD92 z|NJ7x&doh0=jq_!Q0IC;yZfsirCtx4i;K(iXjs}}u7O8hUjERtq_I&TA~KTW&0;HU zs5-juY^^<=pdiKFyLV+26z(8>Vphk9W6_jK=CxJIl8U4a%_+BAXpSU*YWKyk#rqsP zMi5fsaRI&BVmCe{#~@ z=8vXas`u>je5>pnJ2XTXX=7sp-lPle^FvBXM9cNL+yV_AUQ@!z$jJWRArX_IG@%bN z^rT#`v99-Su5r4%yU(#pB*G#hIzKYV;gXSsxgT#DwfWuI#!>00ww_#ES@Hk!g*qTO zc;$F&lAWDhwAyO?m5GV}?p$M*&&~A**_?CixR66!0s?;qx%k$e9yIkLjjq8YE)Kh2 ze6ZE2KGfCmVNvoEp6)FKS5#Ci4|R6lmXeZscKsw_ASNv>3bti-ULMPJ>hTTyZ?7q* zd?Gty4^5tC43me3hOQ51q5Vh`Dys4#B`42v+8D{#tz|1NDIxMH(Ww?MP|5k)+%kri z=P*Tw8{-g^Kl78U33V#7IjOLOYkye-{SxH~^;pXku@z5mwVY!2E@AAiWqUBj_5B9_nVMR%_$C z>(!X)>z2k7QP-=Kg7Gaxm%~9O;6C$&#e3dVkJj!&+M~BUhNmj3ss|f+>C3QkIxMQR zF|DnwNUvYNF8}&fqRIXE=T}SBqs?(l4Gj&QSscqhBRO)Ud~c|sggC1-OVIj;hTa_< z9DHSF7MCSdQ(5Wf>svF5#BI%zZC3QfJ@xgvv3YoOzvafe4S~BvbMX(_ec{GZlww>4 z?h;%mwC(C@hZ6Qwm}#YoKUA@Bn*SIt8Mo>DvpAg6Mua(G%}s7HOrKLyN(vRuTre8W z1EgO0;sN!#bR1_ zhVvAupcZaEq8-WInk+Nv|HOojg~j@Ft5CBn3hE1qn`1OTG73tC?JV#1LJL)2U!QDk zfodK$ztj4A*ySecKQkVw=E_GM8+!j^sIgy&3JF2yww{pR@ftAYoSK^YecmSEbM0BI z-&nJV_FP6$v1hunT(U z)K&+X;A*>|L=Bhd*3yPPe)jDC%a<>w>fh$|W8*;C{%SLguixm>m9L!T$Y9>&`48WD zYobe<$}8Z*2i(*y_uXI55bw73BekO|oFwMMhYxEf*V(+nyO3b)-aQs>PvH+wrROq#_% ze}dq6EzVS1BXJ6te!9|&m!VH&H$t8$)yKrd49|o!m_OCvamwcD<>hd+uI%dOM#I5@ zhs5rA_NK1Be&ZQGb6=@pOB7UAE4?T+=D>;y4tWIy<5DRc_Ff;9;dq*UKb~>=SKy` zNovep^GzNc#+~RwQW~C;5)!YVW$?XOL_+#KoiI~ls|)4$&dzM@lYZP#8HFc?&7D0| zJ`u@J?f+<9&)T;bL0NSV3lrvE8%mdkkN$LWOfr%EBDUP52fHLr`m@{N@4Kvz9}^K2 z5#_-4H|hOALn`7exV^po^`|sFpo3BNBNl1B@A-aYxd*PK$;chdwj6(ak`FE-PNq=@ z8JB8$P+1M@@7vJ~%dn{4t1e6k9^N9A=%N>8DE<72T^@4ocgBvs=l&8H)0Epuyo83&3?!xE$k-!&LZ%3xt62)5-ppPK zy#6}GOaE+xSCE&rr~kVhdg#mNNmkWLP2cbgdL(EV#ZU{}^quZs2u+lvMWm>w1n36) zbbM6TUGn~eCsDtduU*25g`bIcd`84c|GIpe<4)1FR;Xe!;a=Z6%oPOkP|lL_W#pX< z$jM=#prBxX`$t}_NTWpN8}24JyG>+JTaFamX##(d-NKcN|+Vt(Y4h?G1JKCsB^;*bd-bdPZ!WtRYxf+F6 z7r%_U%DjEI`wLH{fP3h)0?PdIzrSme#Ibs8Bxz}B#y?V?Wvb-JT6s;JA8n|b9w;wC zr>J=SolLnOv7ff@(F)=Z&N9!?BQI{Pb)6&@d{QbKCxCEr8rm}N!ss5XHb937e z(96Uhn4Z=qB?CpmxIN(RGVFA5bQ-%~bu@6e%Z>ze;W8X{W_VJ2T=o~mj4Um+7ypWN z1l{X`Ri(eYy6Q^eGM7kT)4z?R+~{Ik(v$e{BdyEMOipEK<#o170kmYdQYt~$^8JH@ zVfTnY;K&&vC4dX~=MsvFil7S+LUkb1AILv1uCW*vA50a{yp4*AQLk4zYQj|f`?o2F z>;9`-qH4uj%5cmivvYGXlH?gvQd0x#>+45Ww#JJzD})$nGc(`p%?mB9uRmgxPyDdP z)3LwQaZJe^_r}}XJ5$UbwY|4jW@}Oa`g41KzpQ*RuY#+G$JRHhM*5J@P&_s^He;yU znMNH!%v;UYlck8(zurVhOq^S3d++Yu;dibKKYrD}jcXOiL1K)mc$C0u8iuqG|Df%nbTyPSnt_?PhPYiv z89hBc1H!|@NrgS@`mHmW)eA$YywAk=`1!+PA1a2Gm$N(kJ2Zp!q5(8jve!Yw0LbJa z6sSihdkar^?S4IiO;5OI9{oV5(-#Hp)3xnvje>)%k%h&hAZ$uzm)&1XzkdDdY`M8+ zaoL`tVN^_GwHhysv!_zNIzJ9fPEMBUiJ@rF(n=-^M@_$?D{bn>rG2JX{FQ?UUsO~_ z9c$h}w>JQnmP4HHr4>Uzc|3u_P78_8zj^#8rrJ5*h+}SE;w#9R&6C7w)DZaS^=+29 zGq7DfGvMZWN=T#=MHv3>PDp-&eGgMp!}6nU9&UVmQ5h`j5E=nHjHI7YlO#SQSJtE$ zcd4`EY`QAnuhHJX0ihjnh{(5R#d|N2$eYZ3vM@@YX^Ph{rISeZ6gU0qt%9k>fvl0q zd2VSTHnD)6d3-wQ(j&%d<|kMKJVYl8Hw>QkO|H5mWprIFv7f_K@X(#2SuODu@@ZiA%t2ufTPRWDXH%sEx^8*V$H~LvurlzJLJ~vmI6e8Z=M9UgIPD{(n z%X^;+GpZ8WzuTK9q7-l$o4I$EBbTsw@gX2OI{J}evuEkl;P>xcX9p{RSy@@o8{fZi zb|wO_OM-%iO$aajaXLjoNr_3=107C19@;+>CFNDH8Q}3Ss;hqt2^>j&G9ooL)0w1; 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zLIM*w2}do_9HQGb+ZX_s-co*adATvrko-0Me5uQ{&hLN4TCMd5tM`{+s>oYee(wKI ilefhCpZ}W%nTiot-2C;HPt!KK4gMB;>mtitvHt?})d3s; literal 0 HcmV?d00001 From c02d009586b9cfe954df22d84ab8a155e9d67b9b Mon Sep 17 00:00:00 2001 From: Daniel Justus Date: Mon, 30 Sep 2024 15:23:13 +0200 Subject: [PATCH 04/11] add GenMS summary --- .../2024-09/papers/2024-09-27-GenMS.md | 47 ++++++++++++++++++ .../2024-09/potm/GenMS/GenMS-diffusion.png | Bin 0 -> 166245 bytes .../2024-09/potm/GenMS/GenMS-pipeline.png | Bin 0 -> 291681 bytes .../2024-09/potm/GenMS/GenMS-results.png | Bin 0 -> 224327 bytes 4 files changed, 47 insertions(+) create mode 100644 _posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md create mode 100644 assets/images/posts/2024-09/potm/GenMS/GenMS-diffusion.png create mode 100644 assets/images/posts/2024-09/potm/GenMS/GenMS-pipeline.png create mode 100644 assets/images/posts/2024-09/potm/GenMS/GenMS-results.png diff --git a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md new file mode 100644 index 0000000..d152c7c --- /dev/null +++ b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md @@ -0,0 +1,47 @@ +--- +title: "Generative Hierarchical Materials Search" +paper_authors: "Sherry Yang, et al." +orgs: "Google DeepMind" +paper_link: "https://www.arxiv.org/abs/2409.06762" +tags: + - LLMs + - diffusion + - GNNs + - materials +potm_year: 2024 +potm_month: 9 +paper_order: 1 # Editor will decide +image_dir: "/assets/images/posts/2024-09/potm/GenMS/" +review_author: + name: "Daniel Justus" + link: "https://www.linkedin.com/in/daniel-justus/" +hidden: true +--- + +### The key idea + +In recent years, machine learning based methods have increasingly been applied to assist the discovery of novel or improved materials with certain desired properties. In this paper, the authors present GenMS, an end-to-end generative model for crystal structures from language instructions. To that end, GenMS combines an LLM to process the user input, a diffusion model to generate molecular structures, and a GNN to predict the structures' properties and select the best candidates. + +End-to-end pipeline for the generation of crystal structures from language instructions. + +### Their method + +The authors argue that data linking the properties of materials to their crystal structure exists at two different abstraction levels: high level information is available as text, while lower level structural information such as atom positions exists in crystal databases. To reflect this, the generative model is split into two components with the chemical formulae of candidate materials serving as intermediate representation: + +1. An LLM trained on materials science knowledge from sources such as textbooks is used to sample chemical formulae that satisfy the user directions. Retrieval augmentation is used to gain additional information and the formulae of crystals from existing databases are provided in the context to avoid generating know crystals. +2. A diffusion model trained on crystal structure databases then generates crystal structures from these formulae. To improve the efficiency of the diffusion model, a simple representation using the 3D position and atom number of each atom in the crystal is adopted instead of e.g. a graph. +Diffusion model for crystal structures. + +As a final step, a pretrained GNN is used to predict the formation energy and potentially other properties of the generated crystal structures and rank them based on this result. + +During inference, a tree search is performed to identify low-energy structures that satisfy the natural language instructions. Here, the number of generated intermediate chemical formulae and crystal structures are hyperparameter to trade off compute cost for result quality. + +### Results + +The main baseline presented in the study is an LLM that is prompted to directly, i.e. without the chemical formulae as intermediate representation, generate crystal structures in the form of crystal information files. GenMS significantly improves on this baseline in all investigated quality criteria. +Furthermore, the authors demonstrate that the model follows simple prompts such as requesting a metal or a material that is not present in a given list. +GenMS results compared to a prompted LLM. + +### Takeaways + +The possibility of sampling materials based on from natural language instructions in an end-to-end fashion is a promising direction for improving materials generation and making it more accessible. However, the authors acknowledge a few shortcomings that require further work. 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Training instability in FP8 due to SwiGLU activation function.
+ + +### Background + +Machine learning researchers, especially in AI hardware companies, have been investigating for the last couple of years which +8-bit floating formats are suitable for neural networks training and inference. The literature on the subject converges towards +the definition of two formats: **E4M3** and **E5M2**. The former being used to represent weights and activations, while the latter +is used for gradients, which requires a higher dynamic range. + +Due to the much smaller dynamic range compared to BF16 (which is commonly used in training of LLMs), FP8 LLM training requires ad-hoc +per tensor scaling using data statistics (usually the absolute-max) in order to keep training stable. + +Most of the FP8 literature has focused on small to mid scale experiments (at most 100B tokens training), and presented in this work, +late stage LLMs training also presents numerical stability challenges, with large outliers appearing in the transformer feed-forward layer. + +### Their method + +As presented the figure above, instabilities appear in late FP8 training of large LLMs. In this work, the authors narrow down the issue +to the quadratic form of the *SwiGLU* activation function when combined with weight alignement. Experimental training data shows that +large outliers are appearing more often during late training due to the correlation between `w1` and `w2` SwiGLU weights (which are uncorrelated initially). + +SwiGLU weights correlation and outliers. +
SwiGLU weights correlation and outliers.
+ +These outliers will lead to underflow or overflow during FP8 quantization when combined with delayed scaling, as the latter technique relies on +the previous batch statistics for optimal hardware usage. In order to circumvent this issue, the authors introduce a new *smooth SwiGLU* activation +function which incorporates channel scaling correction prior to FP8 casting, i.e.: + +Smooth-SwiGLU channel scaling. + +As presented by the authors, channel max-scaling is well suited to hardware accelerator as each chunk of data can be treated in parallel, and the resulting +rescaling can be fused into the FP8 quantization of input activations $x$ and weights $w3$ (third MLP layer): + +Smooth-SwiGLU definition. +
Smooth-SwiGLU definition.
+ +We note that the introduction of the *smooth-SwiGLU* activation preserves the overall FFN definition (from a mathematical point of view): additional channel scaling factors are compensated later in the network in the third MLP layer. Interestingly, Graphcore Research has also adopted a similar approach in our recent [Scalify](https://github.com/graphcore-research/jax-scalify/) work: incorporating additional scaling in neural network to improve numerical stability while keeping the same model definition. + +### Results + +Training experiments on a 7B Llama2 model show the improved stability of FP8 LLM training when using the activation *smooth-SwiGLU*: training loss as well as zero shot downstream tasks match the BF16 baseline. The use of *smooth-SwiGLU* only leads to a small drop of FP8 training acceleration, from 37% to 34%, due to the additional channel rescaling. + +FP8 LLM training with Smooth-SwiGLU. +
FP8 LLM training with Smooth-SwiGLU.
+ +Additionally, the authors also demonstrate that the FP8 E5M2 format can be used for storing Adam optimizer second moment (as presented in previous works, the first moment can be represented using E4M3). diff --git a/assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-smooth-swiglu-training.png b/assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-smooth-swiglu-training.png new file mode 100644 index 0000000000000000000000000000000000000000..0599e1a09df6c23890f41af86fe010dbc1d1bf73 GIT binary patch literal 125907 zcmeFZcRbbq|2KY$P$9_*Ns`LW%vO;-v$wMM-ZLQy*+mGM$FZ_^B+1G=wvfHDvwx4H zKG*m6y>8cU{Biwxop$_T_cEd&C~ z0S^bRM9A58*VF#b&6?`C>JNwx&)B=zDLX|H*UMaB zd0IPN5xiTsJF_Yvnoai3q%;gG!6q?8Lq#@2y}5yj|K4kw1T2nEulb~2rO*7S3XWKF zy`0=%wc6Eg_u#nSZyx zc_-U`CHQ5t&J`xP_KnQPg5uO_+({dOF)fU9ES*|K?Tfp=*zirZVvX~ADIem8YCOlj zeBt|@4|r6UhnE{K98%5^#Z#mrk#poCL?945*H@YH!t{<+VVw2wgj(V>2;i%bO z__1FDt0MA#=Wfw-pFHA((=LPeFoThOQ_3SFH88?&-1_HKRq9>Oh8e;iCq~~gm!*BQ 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z?R@Vy$_oa&VM4V<4@7L%Z3^@wS}lRYUe$eLzcPj(ei7(bx%dsf+;}u>L8J#c87Q=V NY>qluP>{Zf{{k!MqIm!S literal 0 HcmV?d00001 From d6999fbd4ba5eec5d3e0a336cbd7991c45445191 Mon Sep 17 00:00:00 2001 From: Daniel Justus Date: Mon, 30 Sep 2024 15:47:28 +0200 Subject: [PATCH 05/11] fix typos --- .../2024-09/papers/2024-09-27-GenMS.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md index d152c7c..b8cc500 100644 --- a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md +++ b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-GenMS.md @@ -26,22 +26,22 @@ In recent years, machine learning based methods have increasingly been applied t ### Their method -The authors argue that data linking the properties of materials to their crystal structure exists at two different abstraction levels: high level information is available as text, while lower level structural information such as atom positions exists in crystal databases. To reflect this, the generative model is split into two components with the chemical formulae of candidate materials serving as intermediate representation: +The authors argue that data linking the properties of materials to their crystal structure exists at two different abstraction levels: high-level information is available as text, while lower-level structural information such as atom positions exists in crystal databases. To reflect this, the generative model is split into two components with the chemical formulae of candidate materials serving as intermediate representation: -1. An LLM trained on materials science knowledge from sources such as textbooks is used to sample chemical formulae that satisfy the user directions. Retrieval augmentation is used to gain additional information and the formulae of crystals from existing databases are provided in the context to avoid generating know crystals. +1. An LLM trained on materials science knowledge from sources such as textbooks is used to sample chemical formulae that satisfy the user directions. Retrieval augmentation is used to gain additional information and the formulae of crystals from existing databases are provided in the context to avoid generating known crystals. 2. A diffusion model trained on crystal structure databases then generates crystal structures from these formulae. To improve the efficiency of the diffusion model, a simple representation using the 3D position and atom number of each atom in the crystal is adopted instead of e.g. a graph. Diffusion model for crystal structures. As a final step, a pretrained GNN is used to predict the formation energy and potentially other properties of the generated crystal structures and rank them based on this result. -During inference, a tree search is performed to identify low-energy structures that satisfy the natural language instructions. Here, the number of generated intermediate chemical formulae and crystal structures are hyperparameter to trade off compute cost for result quality. +During inference, a tree search is performed to identify low-energy structures that satisfy the natural language instructions. Here, the number of generated intermediate chemical formulae and crystal structures are hyperparameters to trade off compute cost for result quality. ### Results -The main baseline presented in the study is an LLM that is prompted to directly, i.e. without the chemical formulae as intermediate representation, generate crystal structures in the form of crystal information files. GenMS significantly improves on this baseline in all investigated quality criteria. +The main baseline presented in the study is an LLM that is prompted to directly, i.e. without the chemical formulae as an intermediate representation, generate crystal structures in the form of crystal information files. GenMS significantly improves on this baseline in all investigated quality criteria. Furthermore, the authors demonstrate that the model follows simple prompts such as requesting a metal or a material that is not present in a given list. GenMS results compared to a prompted LLM. ### Takeaways -The possibility of sampling materials based on from natural language instructions in an end-to-end fashion is a promising direction for improving materials generation and making it more accessible. However, the authors acknowledge a few shortcomings that require further work. In particular, more specific user input (e.g. "generate a semiconductor"), the generation of more complex crystal structures and the inclusion of further criteria such as synthesizability of the generated material remain challenging. +The possibility of sampling materials based on natural language instructions in an end-to-end fashion is a promising direction for improving materials generation and making it more accessible. However, the authors acknowledge a few shortcomings that require further work. In particular, more specific user input (e.g. "generate a semiconductor"), the generation of more complex crystal structures and the inclusion of further criteria such as synthesizability of the generated material remain challenging. From a8623684ed5f57e60f941c64fcef9a4a04f9bcaa Mon Sep 17 00:00:00 2001 From: Paul Balanca Date: Mon, 30 Sep 2024 15:39:23 +0100 Subject: [PATCH 06/11] Scaling FP8 training to trillion-token LLMs --- .../papers/2024-09-27-fp8_smooth_swiglu.md | 75 ++++++++++++++++++ .../fp8-smooth-swiglu-training.png | Bin 0 -> 125907 bytes .../fp8_smooth_swiglu/fp8-smooth-swiglu.png | Bin 0 -> 159406 bytes .../fp8_smooth_swiglu/fp8-smooth-swiglu2.png | Bin 0 -> 27381 bytes .../fp8_smooth_swiglu/fp8-swiglu-hist.png | Bin 0 -> 190576 bytes .../fp8-training-instable.png | Bin 0 -> 107685 bytes 6 files changed, 75 insertions(+) create mode 100644 _posts/papers-of-the-month/2024-09/papers/2024-09-27-fp8_smooth_swiglu.md create mode 100644 assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-smooth-swiglu-training.png create mode 100644 assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-smooth-swiglu.png create mode 100644 assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-smooth-swiglu2.png create mode 100644 assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-swiglu-hist.png create mode 100644 assets/images/posts/2024-09/potm/fp8_smooth_swiglu/fp8-training-instable.png diff --git a/_posts/papers-of-the-month/2024-09/papers/2024-09-27-fp8_smooth_swiglu.md b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-fp8_smooth_swiglu.md new file mode 100644 index 0000000..71d7483 --- /dev/null +++ b/_posts/papers-of-the-month/2024-09/papers/2024-09-27-fp8_smooth_swiglu.md @@ -0,0 +1,75 @@ +--- +title: "Scaling FP8 training to trillion-token LLMs" +paper_authors: "3 first authors)>" +orgs: "Habana Labs, Technion" +paper_link: "https://arxiv.org/abs/2409.12517" +tags: + - efficient-inference + - quantisation +potm_year: 2024 +potm_month: 9 +paper_order: 1 +image_dir: "/assets/images/posts/2024-09/potm/fp8_smooth_swiglu/" +review_author: + name: "Paul Balanca" + link: "https://www.linkedin.com/in/paulbalanca" +hidden: true +--- + + +### The key idea + +Building upon a recent literature on low-precision FP8 training, the authors investigates the FP8 training +stability of trillion-token LLMs (~20 fold increase over previous published work). Uncovering a new form of +critical instability, they present an improved *Smooth-SwiGLU* activation function which prevents activation +spikes (i.e. outliers) of diverging training of LLMs. + + +Training instability in FP8 due to SwiGLU. +