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bmk#1476: might be good for finetuning tho bmk#1476: but it's <1gb if extracted axiom#3599: :veiAw: Dwarf#6935: How old is the data in the pile? I know it's a compilation of many datasets, but it's hard to find information on when the data was gathered. I think it could be very useful to list the recency of the datasets. StellaAthena#3530: This information is in the paper appendix. Dwarf#6935: Thanks Stella! I didn't think to look there. 𓅬 gabriel_syme 𓅬#3220: ah yes your favorite lab I remember :berk: 𓅬 gabriel_syme 𓅬#3220: all that great work, wasted no StellaAthena#3530: Everything is in the appendix cfoster0#4356: Incidentally, that lab's PI is also the author of the recent CVPR motion barring social media promotion of papers 🙄 cfoster0#4356: Again, great work but feel like some poor calls were made 𓅬 gabriel_syme 𓅬#3220: shocking 𓅬 gabriel_syme 𓅬#3220: imagine being a professor that believes in all this deeply 𓅬 gabriel_syme 𓅬#3220: like it's alright to say 'well, what can I do, I'm forced by this and that' but believe in it lol Daj#7482: Imagine writing "Experts make scientific progress, not the general public" and thinking you're not the villain lol StellaAthena#3530: > Elitism? No good sir, you need to be a level 7 wizard to reach this level of the ivory tower, and we weed out elitism at level 4 Dwarf#6935: "Charles! Call my Captain of the Guard. The peasantry are doing science again." inox#5400: learned about these licenses this week and they're in all the pose modeling code, even in other labs because everyone started using SMPL 🤮 inox#5400: just use a real open source license inox#5400: gross
cfoster0#4356: gimme dat GPL any day cfoster0#4356: But yeah it's infectious in all the wrong ways AI_WAIFU#2844: grabs popcorn 𓅬 gabriel_syme 𓅬#3220: Yay gpt-neo-1.3b finetuning running!! 𓅬 gabriel_syme 𓅬#3220: 10h per epoch seems kinda..nice? 6r1d#4829: How many epochs are there? 𓅬 gabriel_syme 𓅬#3220: I'm going to do 2 or 3 I think 𓅬 gabriel_syme 𓅬#3220: we only have the TPU for a week, 3 days left more or less 𓅬 gabriel_syme 𓅬#3220: and I want to try the 2.7b and 6b before thats done, and then check the distillation code in the future 🙂 &.#0001: https://arxiv.org/abs/2107.03374 quinn#9100: What's this from? 𓅬 gabriel_syme 𓅬#3220: sounded like Pratchett but it's been a while StellaAthena#3530: I made it up quinn#9100: Ah Daj#7482: https://images-ext-1.discordapp.net/external/D8tLMEBWTwhZ6yfwM_IY96gDZM8sI1GRdc1qmUcaNZU/https/i.kym-cdn.com/photos/images/facebook/002/079/819/e14.jpg?width=493&height=671 kurumuz#5695: lmao Louis#0144: @EricHallahan where’s the blog reeeeee Louis#0144: I was waiting for it bmk#1476: there is no blog there is only goose cfoster0#4356: Chill out. It'll be done when it's done 🤠
bmk#1476: I can say with 80% certainty that the blog post will be done before 175B is Louis#0144: I’m just memeing olives ❀#2305: haha looks like someone brought down the web demo for 6b olives ❀#2305: 😦 kinoc#5731: https://bellard.org/textsynth/ <-- emergency auxiliary backup Manny96#3437: Cool idea - Conv-GPT... spatial sequential model. Won't get into it, all good. Manny96#3437: Applies to language models, also. Maark#6960: oh wow this is really cool. the results seem pretty good! they say the hardest part is collecting the group of volunteers for the distributed deep learning Kharr#7888: Lots of work published on conv in Transformers. Check arxiv. charlie#9698: Hi everyone, I know this might not be completely related to GPTJ/neo but I'm getting a very weird bug when fine-tuning a BART model for an HTML formatter (adds HTML tags to any text). When training for more than 3 epochs, it just starts outputting garbage even though the loss and BLEU scores are better... I will appreciate any help/pointers, I explain more about it here: https://discuss.huggingface.co/t/bart-base-generating-completely-wrong-output-after-training-for-more-than-3-epochs/8173 Louis#0144: #off-topic krigeta#6645: So after the new update is it possible to generate stories on the given situation? Louis#0144: What dms#2699: Do you guys have a way to donate sandwich/coffee funds so you can stay fueled up? Louis#0144: no EricHallahan#1051: Nope, it just isn't worth it for us to maintain something like that. 𓅬 gabriel_syme 𓅬#3220: sandwich? I'll have a sandwich nshepperd#2316: launch your sandwich in the air from a catapult while shouting "sandwich for EAI!" and a passing goose will pick it up ~~but not necessarily deliver it~~ Teemochu#8740: Congrats on one weird year! May the next year take you on even more weird turns with the magic of AI. -Archivist#7336: heyo, still learning and looking to clear up a few things as I read. So, training something like gpt-whatever, I know what a dataset is, got that bit down, but what is a parameter in this case? It's my very basic understanding that with the same base dataset you can train a model to some number of parameters and that's where the 117m, 1.5b bit comes from right? Soooo, what's a parameter? and is the _only_ reason not to go big right away time/cost to train or?
-Archivist#7336: If there's some training language models for dummies feel free to point me there, I'm spending all my time at the moment reading about this stuff bmk#1476: more parameters means the model has more capacity so it costs more to train, yeah bmk#1476: I like to think of parameters as knobs on a really really big box EricHallahan#1051: I would say that is a good way to put it. bmk#1476: when you turn the knobs, you change the behavior of the box bmk#1476: in this analogy, a model architecture is a type of box, and of course for different types of boxes the knobs do different things and there are different numbers of knobs, and a trained model is a particular configuration of knob-turns -Archivist#7336: so it's somewhat of an instruction? or scale, like a parameter to set the amount of... idk, red paint I use in this painting? bmk#1476: people are sometimes sloppy about equivocation between the idea of a type of box, a particular box of a certain type that doesn't have the knobs dialed in, and a particular configuration of knobs for a particular box, because it's usually clear from context alembic#5293: The first neural networks did indeed actually have knobs (potentiometers) rotated automatically by motors https://cdn.discordapp.com/attachments/729741769738158194/862741702133153802/iu.png bmk#1476: these correspond roughly to architecture, model, and pretrained model I guess, though people use model to refer to all three of these things sometimes -Archivist#7336: and then if so, where do the parameters come from? the numbers in relation to this confuse me, we're talking in billions so it's not like someone decided on these billions of parameters right? -Archivist#7336: and is it as simple as _there's a nazi parameter_ we will want to turn that one down... ? bmk#1476: so a model with a billion parameters means it has a billion knobs in total bmk#1476: and we use this nice handy algorithm called SGD to help us turn the knobs for us until things work how we want bmk#1476: because nobody has time to turn a billion knobs by hand bmk#1476: well, no, but actually maybe -Archivist#7336: I feel like this is going to take me awhile, you guys have merely cleaned the dirt off a frosted window so far 😄 (I understand the general complexity, thank you for bearing with me) bmk#1476: so conventional knowledge is that the knobs do wacky things and SGD has it all figured out but humans can't interpret it bmk#1476: but there are occasionally papers that identify individual knobs (or PCAs of groups of knobs) that actually do human identifiable things
-Archivist#7336: > SGD has it all figured out but humans can't interpret it that clicks -Archivist#7336: down the SGD rabbit hole I go bmk#1476: see: knowledge neurons for example bmk#1476: recently people have started caring a lot about figuring out what the knobs actually mean bmk#1476: actually I think DNA is a good analogy for parameters Louis#0144: ~~Who said turn it down?~~ Louis#0144: Jkjk bmk#1476: lots of genes don't do single things bmk#1476: sometimes you have a weird complicated thing where you need this and that and that other gene together to do a thing bmk#1476: parameters are generally like that, and it's surprising if they aren't alembic#5293: Maybe folks disagree, but the Andrew Ng online course isn't bad if you want to learn fundamentals. If you just want to start using DL and learning the details as you go, the fast.ai online course is the way to go. (If you haven't already tried either/both :P) CRG#8707: Interesting thing is, you can find that the nazi neuron is actually a mustache neuron and it's fused with the nintendo neuron <https://microscope-azure-edge.openai.com/models/contrastive_16x/image_block_4_7_add_1_0/20> https://cdn.discordapp.com/attachments/729741769738158194/862744822586933268/8f0514e31296d91c862146f4afb526e0.png bmk#1476: also benq for some reason bmk#1476: and call of duty? bmk#1476: and also the words beauty, benefit, borders?? bmk#1476: this neuron is wack bmk#1476: what do mustaches, swastikas, iron crosses, Wiis, beauty, benefit, borders, benq, call of duty, and bumper cars have in common???? CRG#8707: Wii -> mario -> mustache <- hitler <- nazis Hasdino#6050: first time reading the story of eleutherai, gratz to all involved
bmk#1476: what about benq CRG#8707: Something something "concepts so orthogonal/so unlikely to coincide that it's fine to reuse the same neuron" EricHallahan#1051: ~~The logo looks like a mustache~~ CRG#8707: <https://distill.pub/2020/circuits/zoom-in/> https://cdn.discordapp.com/attachments/729741769738158194/862755230454382612/fa971f67bc4f32e39cfb02395d9f5d4f.png bmk#1476: well now you have a fully general explanation for any neuron at all, and you have no explanatory power anymore bmk#1476: neurons can represent both things that are conceptually related, and also things that are totally orthogonal? that's, like, everything bmk#1476: everything is either conceptually similar or not conceptually similar CRG#8707: It's pretty strange, I'd say that these models are just too tiny to not mix concepts, but apparently the bigger CLIP models didn't work well with feature visualization. (The opposite of what the "large models are disentangled / internally sparse" hypothesis would predict) https://cdn.discordapp.com/attachments/729741769738158194/862758244787552277/a8a3fb9e2a11461f4605fab7ad5f5807.png 𓅬 gabriel_syme 𓅬#3220: I love how an intuitive description of NNs turned into deep dive on knowledge neurons :tribalism: TruGerman#6672: Just woke up and saw the ping. I have to say, this blog post is one of the greatest and funniest things I've ever read coming from any kind of scientific organization. You'd think ML researchers are a bunch of grumpy middle aged people with no sense of humor whatsoever, but it seems like that's not quite accurate. Here's to another year of [frantically points fingers in various directions] whatever this is, except this time I'll be here to o b s e r v e, cheers :aPES_Beer: But seriously, thanks for all the...stuff you've been doing, I'm pretty sure I can speak for all the people over at NAI/HAI/Whoeverelsemightbeusingyourmodels when I say you guys gave us hope and saved our AI-deprived asses amidst OAI's tyrannical reign. Keep doing...whatever it is you're doing. Well, time for me to fade back into obscurity:pepepoof: Louis#0144: honk @TruGerman TruGerman#6672: @Louis :goose: 📣 💦 honk Louis#0144: confirmed goose TruGerman#6672: Crap. Teemochu#8740: As I said, "May the next year take you on even more weird turns with the magic of AI." :smugS: EricHallahan#1051: But that isn't possible, it is only 🇩🇪. TruGerman#6672: I should really get my head out of my ass and start learning about...this, sounds like [fun] Dromarion#3383: I've been studying since the beginning of the year. It's hard since the coursework is pretty dense but being able to understand the conversations here makes it worth the effort. TruGerman#6672: Yeah, summer break is coming up which means I'll have a lot of free time
Dromarion#3383: If you want to do the self study route like me, I'm taking the machine learning course by a Daniel Bourke on Udemy. Supplemented by the resources here https://whimsical.com/machine-learning-roadmap-2020-CA7f3ykvXpnJ9Az32vYXva TruGerman#6672: That is one hell of a roadmap, but it should give me an idea of what to do next, thanks! Dromarion#3383: There's a video that walks through everything on it so you can follow along with that. https://youtu.be/pHiMN_gy9mk chilli#5665: This is too big a roadmap imo TruGerman#6672: This is why I prefer physics :luls: TruGerman#6672: Math is way too cursed Dromarion#3383: Roadmap is kind a misnomer here since it's basically a list of resources in the form of a mind map. But yeah it's pretty thicc, it might be easier to navigate in another format. gdawg16#0493: WHAT A GOOD BLOG POST Louis#0144: lmao Louis#0144: WHOS A GOOD GOOSE Manny96#3437: None on Eleuther.ai publications and codebase Louis#0144: I am home so I will retype this here. I am considering making a CV task where I take tuples of sequential pages from manga (3 or 4) and I split them up by panel. Each of these pages is a sentence and each panel is a token. I encode each panel (independently) using a ViT and use the ordering as basically a ground truth. Given sentence permutation, masking, and sentence shuffling, I try to get some visually grounded BART to re-order them in the correct order. Does this make sense as a task? It should capture something about grounding I think- if you can understand the ordering of visual events. Louis#0144: (I put this in the DALL-E discord on accident lmao) Louis#0144: I think the requirement that the panels have to be disjointly encoded is the real benefit here
Louis#0144: I also think that varying size panels in the manga is gonna screw me over Dromarion#3383: Which manga? Louis#0144: Manga109 Louis#0144: its a big dataset of manga scans Louis#0144: they look pretty high quality Louis#0144: I want ERNIEv3 to be really good at anime and manga Louis#0144: fwiw Louis#0144: gonna weeb it really hard Louis#0144: maybe add AO3 Louis#0144: (kidding about the last part dw) Louis#0144: but yeah theres a lot of untapped potential in anime and manga for grounding Louis#0144: nvm apparently manga 109 is just covers Teemochu#8740: AO3 isn't manga Louis#0144: no but its fanfic Teemochu#8740: Yeah true Louis#0144: and crossover stuff actually helps storytelling models a lot Louis#0144: lol Teemochu#8740: AO3 isn't the one that came to my mind first to match with a manga set Louis#0144: ye the ao3 thing was a joke Louis#0144: Im not adding ao3
Louis#0144: y'all can add smut after Teemochu#8740: ~~Add the one with the numbers~~ TruGerman#6672: Louis contributing to the weeb community again, I see Louis#0144: yes. Louis#0144: im deciding to use manhau Louis#0144: theres lots of stuff available Louis#0144: and the paneling sizes are good TruGerman#6672: So you gonna allow us to generate a fitting manga for the garbage we produce in [Insert AI storyteller here]? Louis#0144: i dont work for nai Louis#0144: also I am not interested in image generation Louis#0144: lol TruGerman#6672: Fixed it for you Louis#0144: lmao Louis#0144: im not working at latitude Louis#0144: if thats what youre asking TruGerman#6672: Nah, I was just joking Louis#0144: anyway they have a DALL-E stack 👀 TruGerman#6672: And NAI is a well known example, that's why I used it Louis#0144: im doing grounding research right now Louis#0144: literally exclusively for me
Louis#0144: #carp is controllable NLG + eval. I helped there and thats finishing up. I'm also writing a paper on CLIP. I dont intend to go back to image generation. GANs are a nightmare and I havent learned diffusion models yet TruGerman#6672: Not just GANs Noa Nabeshima#0290: Seems related to https://arxiv.org/pdf/2104.07143.pdf EricHallahan#1051: https://arxiv.org/abs/2104.07143 Noa Nabeshima#0290: did you have the same thought as me at the same time? EricHallahan#1051: No, I just wanted the abstract link. :berk: Noa Nabeshima#0290: ah, that makes sense nostalgebraist#3542: huh, this paper does not mean by "neuron" what i expected it to mean by "neuron" nostalgebraist#3542: (nor what anyone should mean by "neuron," imo) nostalgebraist#3542: > We used the final layer hidden state of each sentence’s [CLS] token as its embedding. [...] For convenience, we identify a neuron with a basis vector in BERT’s 768-dimensional embedding space 𓅬 gabriel_syme 𓅬#3220: You mean the brain? :berk: nostalgebraist#3542: normally people mean "something with a threshold and a nonlinearity" nostalgebraist#3542: so it would make sense for it to "fire" on a very small subset of the data Noa Nabeshima#0290: I think in 'the building blocks of interpretability' (and I think related works?) they optimize the preactivations, before the nonlinearity Noa Nabeshima#0290: I think this is the standard usage of neuron in interpretability papers nostalgebraist#3542: of course an arbitrary basis of 768 dim will mix up all kinds of concepts, you can't parameterize all sentences with 768 knobs nostalgebraist#3542: this is different though, there's not even an activation afterwards (in training there's linear + softmax over 2 dimensions) nostalgebraist#3542: the relevant pre-activation would be in the 2d space right before the softmax Noa Nabeshima#0290: Hmm in my internal definition for 'neuron', an activation/nonlinearity afterwards doesn't seem important. Is there a reason it seems important to carve up wordspace that way as opposed to this way? Noa Nabeshima#0290: Also apologies if I'm misunderstanding you somehow
nostalgebraist#3542: (tangent?: looking at "building blocks of interpretability," it sounds like they optimize post-activation, given the comment "As the name suggests, non-negative matrix factorization (NMF) constrains its factors to be positive. This is fine for the activations of a ReLU network, which must be positive as well.") nostalgebraist#3542: i have a few related intuitions about this... one of them is that the nonlinearity picks out a preferred basis nostalgebraist#3542: so there's actually a "neuron 1," "neuron 2," etc nostalgebraist#3542: (admittedly there are a few other things that break the symmetry that in NNs, like adam and layernorm) bmk#1476: hot take: neurons are the wrong abstraction in NNs and it's a bad thing that people focus so much on them Noa Nabeshima#0290: Ah, good catch! I don't know where that belief came from. Maybe there's a later blogpost where they switch over or something in the lucid documentation or maybe I'm remembering writings about optimizing pre-softmax floats instead of probabilities. janus#0150: Looking only at the post-activation risks losing a significant amount of information, no? bmk#1476: for one, when weights are shared, like in a transformer and a CNN, people often equivocate between whether each individual activation is a neuron nostalgebraist#3542: also, when the nonlinearity is not symmetric, it defines a concept of "being activated by an input" with no corresponding concept of "being anti-activated" nostalgebraist#3542: which makes sense for concepts, there's generally not like, exact Anti-Questions-About-Song-Titles and such things not in the same way that Questions-About-Song-Titles are real things janus#0150: Like if I want to search for 'concepts' in a networks brain, it could make sense to look at post-activation values, as these are aggregates of upstream information, or at pre-activation vectors, because this is data the network has learned to group together. bmk#1476: also if you have any activation function that doesn't look vaguely like a sigmoid, which is basically every activation function that anyone still uses these days (sorry schmidhuber), the analogy doesn't make much sense either bmk#1476: also while we're at it, the idea of layers is too ill defined and outdated as well janus#0150: Could you elaborate? I think of a transformer as a feedforward network which is happening in multiple, discrete, sequential steps. bmk#1476: do you count an entire transformer block as a layer, or each ff layer inside those blocks as a layer? is attention a layer? are activation fns layers? if you think that something has to have parameters to be a layer, what about parametric activation fns? what do you do about skip connections, do they count as not adding any layers? if so, what about something with tons of weird connections like inception? do you add up layers that are in parallel too? in which case, any linear layer can always be broken into two linear layers with a concat. oh and also what the heck do you do about RNNs, is each time step a layer? there are so many edge cases bmk#1476: I can think of like 5 different justifiable answers to "how many layers does gpt3 have" nostalgebraist#3542: i think of "layer" as "the unit such that the network looks like [input adapter] + N * [layer] + [output adapter]" nostalgebraist#3542: some networks don't look like that, but it's a meaningful concept
janus#0150: Great list. That makes sense. I think there is a useful abstraction splitting the transformer into discrete blocks, but the word layers could be imprecise and not generalize well. Could you explain how the skip connections work in GPT-3? bmk#1476: a resnet38 has 19 pairs of conv layers with skip connections, does that make a resnet38 have 19 layers? bmk#1476: (or something like that, I don't remember the exact details of what the ends of it look like, but you get the point) nostalgebraist#3542: maybe? i'm not too tied to the terminology nostalgebraist#3542: just, you know, there really is a sense in which gpt3 consists of something copied 96 times bmk#1476: yeah but there are also many other numbers that could be argued to reasonably represent a number of something in gpt3 in a way that it's hard to draw a crisp line to say which of the numbers are admissible bmk#1476: like I think counting the ff and attn layers each as one layer is entirely reasonable nostalgebraist#3542: do they all have the property where you can write most of the network like `[thing() for _ in range(N)]`? nostalgebraist#3542: with the FF and attn you need to pass an arg telling `thing()` which one it is kindiana#1016: what if they really are one layer (with parallel ff+attn) :berk: Noa Nabeshima#0290: https://www.lesswrong.com/posts/yA4gF5KrboK2m2Xu7/how-an-algorithm-feels-from-inside bmk#1476: well, that implies resnet38 has 19 layers and VGG19 has 1 layer kindiana#1016: also, I don't think layer counts really mean anything lol bmk#1476: that's .. what I've been trying to argue kindiana#1016: theres almost always a more precise way to characterize whatever quantity you are trying to say nostalgebraist#3542: fair enough regal-algorithm#6085: hey! The intro in #rules tells me to introduce myself if I want to get involved, so going ahead and doing that. I am Andrey Kurenkov, a 4th PhD student at the Stanford Vision and Learning lab. I have mainly done research on learning algorithms for robotic manipulation (deep RL sort of stuff , with some supervised learning more recently). I just read the one year retrospective piece and found it pretty inspiring, so though I'd get on here and see if I can get involved. More info here: https://www.andreykurenkov.com/ Louis#0144: Firstly welcome! Louis#0144: Secondly, what tickles your fancy?
Louis#0144: what would be ideal for you to be involved in? Louis#0144: I do storytelling research mostly so I dont think I am personally a good match for a project for you to get involved in Louis#0144: but theres plenty of projects going on regal-algorithm#6085: hmm good question. I am not really aware of what projects there are. I guess something I could do with a commitment of a few hours per week that is useful, get my toes wet so to speak, so perhaps helping with some maintenance grunt work to start with. Louis#0144: >maintenance grunt work to start with. Louis#0144: someone get the infra work out Louis#0144: lol Louis#0144: jkjk Louis#0144: Youre probably better off talking to bmk then I think Louis#0144: @bmk Louis#0144: oh wait actually we have short term ish interpability projects if that is of interest Louis#0144: theres a job board Louis#0144: "job" Louis#0144: lol Louis#0144: https://github.com/EleutherAI/project-menu/projects/1 regal-algorithm#6085: oh well well looks like I should check that out 😛 AI_WAIFU#2844: Also feel free to pitch you're own project if you're willing to put the work in. AI_WAIFU#2844: We have tonnes of compute and a lot of it sits idle. AI_WAIFU#2844: The main bottleneck is individuals willing to see projects through from start to finish janus#0150: (fyi looks like the project-menu is out of date. There are many things missing)
regal-algorithm#6085: aint that always the case... Louis#0144: oh uh that reminds me Louis#0144: I think rotoBART is ready to train if theres any compute sitting around Louis#0144: I'll talk with Stella tmrw regal-algorithm#6085: actually, I did have one idea for a side project i've wanted to do... basically train a GPT-type model to go from short (~1 paragraph, like on rotten tomatoes) summaries of movies to their full plots (like on wikipedia). Not too hard to set up, just some scraping to get the dataset and then presumably fine-tuning a pre-trained model. Would that be something that fits? Idk how ambitious a project should be lol Louis#0144: 1) wikiplots and exists and is a really good dataset Louis#0144: no need for scraping Louis#0144: 2) I've tried this and you get really weird results Louis#0144: what works way better is to go from summary + plot outline to wikipedia plots Louis#0144: so using 6b as a seq2seq model regal-algorithm#6085: ah interesting... Louis#0144: plot outlines are also available in wikiplots Louis#0144: but the results you get are weird nevertheless... Louis#0144: not really publishable I think Louis#0144: 🤷‍♂️ Louis#0144: still would be a very fun side project if you want me to guide you through it Louis#0144: you could do it in a week easily regal-algorithm#6085: yeah, maybe a good one to get my toes wet / get a better idea of what else I could do Louis#0144: awesome regal-algorithm#6085: and even if not publishable might be fun to write up in a blog post? just sayin, I enjoy doing that too
Louis#0144: yeah probably EricHallahan#1051: Welcome! regal-algorithm#6085: in any case, a good test to see if I can actually contribute meaningfully given time constraints etc. Louis#0144: does anyone have that recent paper Louis#0144: that uses a game theoretic based tokenizer Louis#0144: its really weird Louis#0144: I cant find it Louis#0144: but they show how well it performs against TDIDF tokenizer Louis#0144: ah found it Louis#0144: https://aclanthology.org/2021.naacl-main.223/ Noyal#0385: By coincidence, I'm also a PhD student working on (sometimes robotic) deep RL who read the anniversary post today and was inspired to get involved! Name's Riley Simmons-Edler, I'm a 6th year at Princeton. I like EleutherAI's mission and I'm curious about side projects to distract myself from thesis writing and the job search. 😅 Noyal#0385: I've had some ideas about trying to get two language models to prompt-engineer each other into saying specific things kicking around for a while, though that might be a big project to take on. Noyal#0385: *via RL chilli#5665: Anything that has a "via RL" in it usually ends up becoming a big project haha EricHallahan#1051: Friendship ended with RL, sequence learning is my best friend. bmk#1476: RL is kil Louis#0144: oh cool Louis#0144: my coworker did this Louis#0144: do you know Zhiyu Lin?
Noyal#0385: Oh cool! I don't think I know them, no Noyal#0385: Did it work? 😛 Louis#0144: id have to ask him Louis#0144: I cant find his paper Louis#0144: but i am pretty sure it did Louis#0144: it was as an ablation in soemthing else though Louis#0144: so it didnt work particularly well Louis#0144: LOL Noyal#0385: Lol, guess that's pretty telling Noyal#0385: Outside the rare case where the ablation ends up becoming the main method Noyal#0385: The general thought was that it could be a cheap way to bootstrap a conversational agent that can have a conversation with some objective in mind (get the other guy to give you information/think positively of you, etc) Em Elle#8886: Anyone ever think we might not need really intelligent chat bots or companion bots? because it's very likely that human conversational abilities will degrade over time, because of a deteriorating culture? and outside factors such as social media ? My prediction is that something like GPT3 or GPT2 would converge to acceptance as Companion* AGI as we move forward through time. Thoughts? this is what I am finding building what I am building* Louis#0144: really smart chatbots is going to be massive for the sex industry Louis#0144: unironically bmk#1476: > deteriorating culture warning that this topic often leads to politrib Fessus#9563: I maintain that this is going to lead the the end of the human race Em Elle#8886: that's the funny part, if we ask the people we would be building these bots for they really don't care about that. At least from my sample size, of 20 who did the poll
Louis#0144: lol Louis#0144: storytelling AI is going to be all erotica Louis#0144: and ive accepted this Louis#0144: im working on getting a dataset for ERNIEv3 rn Em Elle#8886: yeah I can see that being a good use case but sex bot's probably could run on 2010's technology Louis#0144: and im setting it up so it works really well with manga/manhua Louis#0144: for uh Louis#0144: reasons Louis#0144: because I know the audience thats going to be using it Louis#0144: lol Louis#0144: like a storytelling seq2seq model that is knowledge driven? pfft id be lying to myself if I said the main use case for that isnt obvious bmk#1476: manhau? you mean manhua? Louis#0144: I was actually floored today when in a grant proposal my advisor actually mentioned and discussed sex work chatbots Louis#0144: lmao Louis#0144: yeah typo Em Elle#8886: that's probably what this tech will be used for tbh, thats sorta what I am using it for Fessus#9563: “If you want a picture of the future, imagine virtual anime titties smothering a human face—for ever.” -George Orwell, probably Em Elle#8886: LOL yeah that's the future for sure 😉 atleast in my project eventually Noyal#0385: IIRC The big AI Dungeon user prompt leak a while back suggested ~50% of user content on that site might be pornographic (or at least NSFW), so the internet has already shown that this will happen. 😕 inox#5400: showed this has happened and people won't pay much for it
Em Elle#8886: I guess what I am trying to say is that we are probably at the pinnacle of the technology, we are waiting for people to catch up culture wise and mind set wise. Em Elle#8886: If we hit an AI wall or winter were fine, people will catch up inox#5400: the pinnacle of sex chatbot technology? bmk#1476: I never thought people could get off to a chatbot, guess I need to refine my world model inox#5400: feels like sex workers already got priced out of generating unbranded content a long time ago, now they have to manufacture authenticity with the porn to make money Em Elle#8886: probably 2010's rule's based technology or just using GPT2 in a very short chat conversation mode bmk#1476: what happens to the porn industry once we can generate videos end to end? bmk#1476: of all industries related to video, seems like porn will die first Em Elle#8886: I mean that can be done today, the company that builds the pipeline process and automates most of it with programming will win not the AI researchers who cracked the model bmk#1476: don't remind me :withered: Noyal#0385: "the AI researchers who cracked the model" were still a necessary prerequisite for this to happen, don't forget 😀 Em Elle#8886: I know the sad part is, it won't be their names in the news bmk#1476: tfw nobody cares about the researchers Noyal#0385: Researchers gotta look out for each other 🙂 bmk#1476: meh who cares about the news, the part that matters is that they will probably get paid a pittance for their hard work Em Elle#8886: This is why I left my ML masters program I saw that worrying trend, idgaf about research mentality in the business side bmk#1476: can't pay rent with news mentions anyways Em Elle#8886: this too Em Elle#8886: you get paid with the networking and social opportunities for delivering bmk#1476: it's a crime how little grad students get paid
inox#5400: there's zero money in random generated videos, all the performers have onlyfans and other revenue streams Em Elle#8886: it's because its a pyramid scheme bmk#1476: the entire academic system is totally fucked EricHallahan#1051: We just need to make sure that we are not paperclipped, that's all. It isn't like that is hard or anything. Em Elle#8886: I agree, was apart of it haha Em Elle#8886: what is paper clipping ? bmk#1476: Eric explain paperclipping! bmk#1476: the best way to learn is to teacg inox#5400: oh no this discord is dedicated to optimising a utility function to maximise alignment memes EricHallahan#1051: We are referencing the Paperclip Maximizer thought experiment. Em Elle#8886: OHH I remember this story bmk#1476: go on, explain what a paperclip maximizer is bmk#1476: and why we'd expect one to happen EricHallahan#1051: Man am I rusty on this. EricHallahan#1051: That is what I get for staring at HTML for two weeks. bmk#1476: you need to be able to answer questions about alignment in your sleep Em Elle#8886: I remember the story, I disagree with it, I think it's more likely that an AI will become an automated taste maker since that's more of an important aspect to society, and we will be making decisions not on our own, but be primed by an AI to make the decision EricHallahan#1051: I haven't done much alignment work unfortuately. bmk#1476: then start now Em Elle#8886: I think the thought leader in that field is disconnected from reality
bmk#1476: never too late bmk#1476: I disagree with your disagreement EricHallahan#1051: I am planning to sooner rather than later. Em Elle#8886: but... that's just a theory Em Elle#8886: that is okay, I accept your disagreement and understand your stance inox#5400: Yudkowsky is a very stable fanfic author! bmk#1476: just say whatever you know and try to think up answers as you go along AI_WAIFU#2844: wait wat bmk#1476: [sudden interest from AI_WAIFU] Em Elle#8886: @AI_WAIFU you didn't respond to my message, I guess it's not what you are interested in eh ? AI_WAIFU#2844: I'm really bad about that sorry Em Elle#8886: I figured you were busy no worries! EricHallahan#1051: IIRC, it is an underdefined objective function (the simple concept of "maximize paperclips") that leads to an ill-fated outcome, since the implicit constraints that we would expect to to be there were never defined? bmk#1476: good - what are some examples of these "implicit constraints"? EricHallahan#1051: One could be "we only need as many paperclips as sheets of paper?" EricHallahan#1051: Or not turn everything to paperclips lol chilli#5665: "don't kill humans" bmk#1476: shhh EricHallahan#1051: Well that is the obvious one. EricHallahan#1051: I was trying to think deeper
bmk#1476: what chilli said is what I was hoping you'd say lol bmk#1476: ok so I want to drill into this one EricHallahan#1051: Oh, that was my gut response, I just failed to say it. :grimberk: bmk#1476: let's say I ask the AI to maximize the following function: min(1000, number of paperclips) bmk#1476: so basically I want it to make at most 1000 paperclips bmk#1476: how could this go wrong? bmk#1476: once it makes 1000 paperclips it's done and finished, right? how is it still unsafe inox#5400: paperclip stockpile security? EricHallahan#1051: It destroys them. EricHallahan#1051: It can then create as many Paperclips as it wants. bmk#1476: uhh bmk#1476: no? EricHallahan#1051: ¯\_(ツ)_/¯ bmk#1476: because if it destroys paperclips it's reward goes back down again Em Elle#8886: does the AI know what the paper clip is made from? and constraints on the supply? I guess you could just tell it to do something make 1000 paper clips without impacting the environment or human life. EricHallahan#1051: I am pulling everything out of my ass here. bmk#1476: I'm trying to make you think EricHallahan#1051: And I am thinking hard.
bmk#1476: let's assume for the moment that what a paperclip is is well defined bmk#1476: which isn't trivial but makes this case easier chilli#5665: \|\| might still be easier to make paperclips in an unsafe manner? \|\| Em Elle#8886: so stop once 1000 paper clips is achieved ? chilli#5665: not sure Em Elle#8886: LOL this ^ bmk#1476: that's one thing yeah bmk#1476: there's also another thing that can go wrong chilli#5665: hmm StellaAthena#3530: It could destroy paper clips until it’s negative counter overflows bmk#1476: that's way too out ofthe box lol chilli#5665: lol, I was considering saying that as a joke bmk#1476: think more inside the box someKindaBean#8471: That's the whole premise (kinda) of this web game: someKindaBean#8471: https://www.decisionproblem.com/paperclips/index2.html bmk#1476: hint: what happens to the expected reward if the AI isnt certain of how many paperclips it'll have chilli#5665: is it that its behavior is unpredictable? chilli#5665: Like, once you hit 1000 paperclips the AI still has no incentive to do one thing or another bmk#1476: remember, it's taking actions to maximize expected state-action value chilli#5665: hmm, in that case
chilli#5665: what somebody said above about stockpiling chilli#5665: or more extreme options to reduce the variance bmk#1476: yeah that's another problem but even if we can make it shut down the moment it gets to 1000 expected reward bmk#1476: yup chilli#5665: seem accurate bmk#1476: so when the model thinks it has a 0.99 chance of having 1000 paperclips, its reward is 990 bmk#1476: but it could be 0.999 certain by taking more precautions bmk#1476: or 0.9999 certain someKindaBean#8471: You could also extrapolate to a problem that isn't as easily defined with a simple min(x1,x2) statement chilli#5665: hmm, this assumes it's an RL agent though bmk#1476: i mean expected reward is pretty fundamental for any kind of agent chilli#5665: hmm bmk#1476: unless youre talking about non agentic optimization which idk chilli#5665: yeah, I guess you're right chilli#5665: it's not so obvious to me that the methods of minimizing uncertainty are necessarily dangerous Teemochu#8740: I'm mildly convinced AID would have eventually had a similarly-sized competitor even if the only thing they did was (with few false positives) filter out a couple of subsets of NSFW. chilli#5665: but I agree that they could be dangerous bmk#1476: well it might think there's a 0.0001% chance its sensors are faulty so itll buy a redundant set of sensors to observe the paperclips bmk#1476: but there's a chance that those are faulty so itll buy even more bmk#1476: and then it turns the entire earth into a gigantic system for being 99.9999999999999999999% certain that there are more than 1000 paperclips
chilli#5665: yes, but there's also the change that by doing such suspicious behavior it'll be shut down or impaired in its goal Teemochu#8740: It wouldn't have been nearly the lighting-in-a-bottle NAI was though; AID shot themselves in both feet and then took a stimulant to increase the bleeding. Em Elle#8886: What I don't like about the story, is that it's unrealistic and doesn't even capture where the state of the art of robotics is heading, and it appears that robot's Boston dynamics like SPOT and it's humanoid equivalent are not even being used to do any labor and the company is a money sink hole, despite being state of the art. Louis#0144: Do stimulants increase bleeding Louis#0144: lol Louis#0144: I was not aware Teemochu#8740: Just a guess because heart rate/BP Em Elle#8886: if they are cut with some kind of rat poison yeah bmk#1476: who said anything about robotics? that's just a convenient way to talk about something concrete bmk#1476: in realityit'll probably be something boring like "make more money on the stock market" inox#5400: robotics profs I know all say boston dynamics is just tuned control theory that won't scale (although that was 4 years ago they said that) Louis#0144: It is impressive tbh Em Elle#8886: For the control system yes, but not the vision system I am sure that uses some kind of DL bmk#1476: i think there's a really good chance the first ai will have some variant of "maximize money in this bank account" as an objective lol EricHallahan#1051: #off-topic, but Honda ending it's robotics program is kind of a big deal. They are either totally calling that humanoid robots are not going to useful within the next 20 years or they stand to loose a lot in opportunity costs. EricHallahan#1051: That kinda means a lot to that industry. guac#4716: humanoids seem so inefficient for a task as streamlined as car manufacturing lol EricHallahan#1051: They were never for manufacturing? guac#4716: what were the humanoids for? Em Elle#8886: yeah I don't think humanoids are efficient for much, I think businesses in the robotics industry optimize for fast deployment and specialized motions
bmk#1476: anyways I never liked robotics at all so nothing of value was lost AI_WAIFU#2844: fucking, obviously Em Elle#8886: this would be true, and a better story to tell that isn't unrealistic or make the industry sound crazy, unless that was the authors intent to go viral. I personality couldn't relate to the paper clip story mainly because it was so detached from reality. guac#4716: ah population control. i see i see Em Elle#8886: haha Louis#0144: He isn’t wrong EricHallahan#1051: https://en.wikipedia.org/wiki/Honda_P_series Em Elle#8886: haha it's just funny that it was said 🙂 Louis#0144: Lmao bmk#1476: I think "industry" is the wrong word lol bmk#1476: and alignment has never been good at outreach bmk#1476: ~~because all people who work on alignment are antisocial nerds like me~~ Em Elle#8886: Sorry by industry, I am referring to Machine Learning field in general bmk#1476: oh bmk#1476: most of ML doesnt give a shit about alignment lol bmk#1476: it's sad but it is Em Elle#8886: this is true triggerhappygandi#0001: On the bright side, joining eleuther made me focus more on safety/alignment. Hopefully it did the same for all of us here :hap: Louis#0144: Safety a lot Louis#0144: For me
EricHallahan#1051: I had no interest in AI until arriving here lol triggerhappygandi#0001: Before eleuther my view on AI safety was "cranks on twitter trying to politicize the shit out of a niche field" Now it is "better study that lest we all die painfully" marksaroufim#6706: what were your favorite references on AI safety? I had trouble finding good stuff googling bmk#1476: "cranks on twitter trying to politicize the shit out of a niche field" is very much still a thing lol bmk#1476: those people just happen to be different from the people who actually care about not gying a horrible painful death from 🖇️ bmk#1476: rob miles' videos bmk#1476: hands down the best resource marksaroufim#6706: Thank you @bmk ! Louis#0144: @bmk why does venture beat keep saying we’re releasing neox in august bmk#1476: i dunno bmk#1476: we arent lmao Louis#0144: I’ve seen two articles with this now Louis#0144: Yeah wtf alexyz#3459: what is the chance that bmk is training neox rn secretly and going to release it in august is Louis#0144: 40% bmk#1476: I am the least likely person to do that lmao bmk#1476: lazy af bmk#1476: also, busy with interview prep bmk#1476: but it's funnier to say that I'm lazy
triggerhappygandi#0001: yes but my view on safety isn't affected by them now triggerhappygandi#0001: stuff like this is what those cranks don't discuss https://cdn.discordapp.com/attachments/729741769738158194/862927580049965086/unknown.png triggerhappygandi#0001: rather they will talk about social issues which aren't the primary concern bmk#1476: this is what you think of as a very good central example of alignment? triggerhappygandi#0001: no it is one that is very apparent bmk#1476: ? triggerhappygandi#0001: and exists _right now_. Misaligned superintelligence is still aways to go triggerhappygandi#0001: We haven't even solved this comparatively easier problem bmk#1476: .. i really need to write that blog post someday bmk#1476: "we're still far away from being able to go to the moon! plus, we cant even safely build a building 2km tall without it collapsing, and the moon is way more than 2km away! we need to solve safe building before we can even think about going to the moon safely" Louis#0144: I bet there’s more geese on the moon triggerhappygandi#0001: I didn't say that. I said aligning LMs today is the step towards that. Louis#0144: :3goose: triggerhappygandi#0001: And we haven't even figured out how to align a 12B model Louis#0144: @triggerhappygandi we have an Ernie discord now with a special channel just for geese bmk#1476: you say that as if aligning a 12B model is a prerequisite to superintelligence alignment Louis#0144: Maybe it’s too dumb to align triggerhappygandi#0001: It isn't? I always assumed aligning LMs is a step in that direction Louis#0144: Is that what you mean? bmk#1476: "we cant even safely build a building 2km tall without it collapsing, how can we possibly get 300000km to the moon safely?"
EricHallahan#1051: Or we could just ask the model to be nice. bmk#1476: im not saying that aligning LMs is totally useless, but it's not at all a priori obvious that it will be useful, and youd have to make a clear explicit argument why it would be the case triggerhappygandi#0001: Based off of this https://www.lesswrong.com/posts/PZtsoaoSLpKjjbMqM/the-case-for-aligning-narrowly-superhuman-models and also, _I feel this is the case_. Since GPT-N could probably be AGI. bmk#1476: that post argues for something way more nuanced Louis#0144: Personally I still think scaling laws for transformers are gonna shit themselves before AGI even though all evidence says otherwise Louis#0144: I’m hopeful lol triggerhappygandi#0001: Plus, my original argument was that rather than focusing on this topic, the twitter crank researchers focus on how much pollution Switch Transformer _might have caused_ because google's datacenters already are carbon neutral. Louis#0144: What confuses me is how much bigger cv is even though I feel like NLP is closer to AGI Louis#0144: It’s probably just Bc it’s hard to monetize NLP Louis#0144: lol triggerhappygandi#0001: yeah but one of the points is specifically that smaller aligned models will guide us through to next stage. bmk#1476: yes but not all alignment work on small models is created equal 𓅬 gabriel_syme 𓅬#3220: well maybe because everyone thinks of it as text alone nshepperd#2316: what they don't realize is everything is text, even people 𓅬 gabriel_syme 𓅬#3220: yeah that works too, if I understand how you mean it sea_snell#0243: NLP is operating on more abstract data, so if it’s closer to intelligence, with cv you have to deal with the raw signal more 𓅬 gabriel_syme 𓅬#3220: my point is that everything can be language, but the actual 'a LM writing a blog post!' is not the only application of language. but all that, we're only now starting to find out (at least I am) sea_snell#0243: The mind blowing thing in the clip paper was deep in the appendices
𓅬 gabriel_syme 𓅬#3220: I never thought I'd be making designs using LMs for example, I was all in DALLE just 3 months ago. Now I'm totally on the LM camp sea_snell#0243: They had non trivial sentence embedding from text rendered on an image, like it could represent sentiment sea_snell#0243: https://cdn.discordapp.com/attachments/729741769738158194/862959693927153664/image0.png sea_snell#0243: What if in a couple years all NLP will just be processed via rendered text on screen, don’t even have to think about tokenization. But ig there’s a trade off cause you would want to think about font chirp#4545: curious, what makes nlp hard to monetize? triggerhappygandi#0001: how so chirp#4545: https://twitter.com/isosteph/status/1413410298472456194?s=21 ethan caballero#6044: yes, videogpt will be able to do everything that dall-e, clip, & gpt-3 can do simultaneously. CRG#8707: Why would videogpt be as good as text as GPT-3? Teemochu#8740: The flip side is this may represent a limitation on superintelligence rather than an alignment problem per se Teemochu#8740: basically the opening stanzas of the GIGO problem of generative AI Teemochu#8740: and I wouldn't say it's particularly wrong to give a user what they want... in this case it's a poor assumption that the user actually wants buggy code (rather, the user wants the AI to be above his/her own intelligence), but fixing that issue from an "alignment" lens rather than a "capability of writing good code" one feels dangerously adjacent to cases where some would prescribe using the "alignment" lens to intentionally not give the user what they want. Tinytitan#5596: @Daj https://news.ycombinator.com/item?id=27780786 Teemochu#8740: Is that by the... yeah looks like it is Daj#7482: Absolutely wild Teemochu#8740: from a reply > This is far-fetched sci-fi problem invention. The only real danger of AI in the next 1000 years is in things no one in the field is seriously addressing: use of AI in things like law enforcement, trained on bad data, to accelerate and justify existing systemic biases. I'm not exactly sure what line of thinking produced this actually. (The "things no one in the field is seriously addressing" part specifically, as pretty much every popsci/journo resource I've seen on "AI safety" other than Miles focuses on exactly the thing this person is claiming isn't being taken seriously) Daj#7482: ~~I hope we didn't make him update _too_ much since we're still an outlier in taking alignment seriously lol~~
Daj#7482: But crazy to think Eliezer himself knows about us now (and probably isn't _maximally_ happy about it but not maximally _un_ happy either) Daj#7482: The answer is :tribalism2: Teemochu#8740: I guess it's the same line of thinking that says "too many rubes, why won't anyone solve the Rube Problem" when there's one rube among 99 bleggs thenightocean#6100: IMO I would say he is more at less at peace with the future and how it will unfold. He did his contribution and now it depends on everyone else to try to avoid a bad outcome. I doubt he really obsesses about good or bad actors anymore. Daj#7482: idk I conceptualize Eliezer as pretty agentic Daj#7482: but who knows Teemochu#8740: there's nothing to be proud of until we turn catgirl theory into catgirl practice thenightocean#6100: I kinda got the impression he stopped being very agentic. he now just wants to grill and occasionally shitpost on twitter/facebook Teemochu#8740: >titter Daj#7482: as hilarious as it is to imagine Eliezer saying "I just wanna grill", I don't think that's likely, I'm pretty sure he's actively working on stuff with Nate, or at least was until that got axed thenightocean#6100: fair. He is still reasonably young (and in best shape in his life lol) CSEdd#5494: Hey! Which groups/projects are working on AI alignment or security? Keen to get involved! Daj#7482: We have a bunch of general discussions in the alignment channels. I personally lead the #deleted-channel project which is something of empirical prosaic alignment work Daj#7482: Related to the kind of work Christiano did at OAI Teemochu#8740: nice cutoff google https://cdn.discordapp.com/attachments/729741769738158194/862988635409874944/VDDhbSt.png Daj#7482: There are also a handful of interpretability projects (or at least ideas) floating around Teemochu#8740: (was looking up his age and saw this in the knowledge card) Daj#7482: (unfortunately I need to hop on a bus now, if you have any questions @CSEdd hmu any time) distractedm1nd#2062: Hey everyone, I work on DNA Data Storage Research in Germany but have a CS/ML + Neuroscience background. Going to be lurking over the next few days to see how I can maybe fit in 🙂 triggerhappygandi#0001: title of the story harry imagined in his head for 7 years
suh#2879: LOL @copilot spirit-from-germany#1488: @Louis i also like the idea of ernie 3, but i am wondering what kind of knowledge graphs are available as training data sets freely goolulusaurs#1571: I just read the retrospective. Damn what a year y'all. What you guys have accomplished is amazing. :bigbrain: ethan caballero#6044: Hm, Eleuther has all the transhumanists excited as of late: https://twitter.com/anderssandberg/status/1413259464862539781 https://twitter.com/tobyordoxford/status/1412442608245293060 Daj#7482: Well I guess we know what Anders is into now rygaard#8558: Hi want to introduce myself here - I am here because I am working on establishing an art/tech festival in Denmark. I saw the Anders Sandberg VQGAN-CLIP article and was amazed. I am curious whether someone is combining these techniques and artistic approaches with study of biases / diversity in AI - eg. by visualizing gendered / political statements. I would love to host such experiments at the festival. I will have a look around here - bear over with med if my technical insights are limited at times. Daj#7482: You should introduce yourself in #art too. You can also use our bot in #the-faraday-cage-archive to make art, but we'd ask you to please not try to make too risque stuff in public lol rygaard#8558: Tanks @Daj for suggestions glucinater21#0869: Hey everyone I’m Adam, an incoming college freshman going into computer science engineering. If you guys ever need some extra man hours, I’d be happy to help on a periodic basis( I intern full time at the moment). My skills include webscraping with scrapy and requests/beautifulsoup, basic api creation with fastapi, and basic machine learning with py torch,tensor flow, and sci kit learn. I know I may not be of much use but I’m always happy to learn! bieker#8988: Hey everyone, I'm Jacob, I'm a research engineer at Open Climate Fix working on using ML for solar forecasting with satellite images. I've worked quite a bit with PyTorch, building data pipelines, and using multi-modal models. I've mostly worked with vision stuff, and am happy to help wherever! joaogui1#8461: Starting in 16 minutes! https://www.youtube.com/watch?v=NfvYufQwA_o Deleted User#0000: Hi! I'm Jamie. I helped co-author layernorm, show attend and tell, etc. I left google 5 months ago to start a company and a small farm. I'm slowly getting back into research and would love to collaborate in the future (multimodal learning, generative models, RL). Currently playing with vector analogies in clip Sid#2121: we ❤️ layernorm Kia#2550: Wow We have a whole lot of new people😄 thenightocean#6100: Welcome @bieker , @glucinater21 and @Deleted User ! Looks like you have some great skillsets.
triggerhappygandi#0001: Hot damn. What's your current startup about? Deleted User#0000: yay 🙂 when we wrote the paper transformers didn't exist yet and all the focus was on improving RNNs. interesting how things worked out CRG#8707: Layernorm seems to hold up against the other x-norm variants for training GPT-style transformers. https://discord.com/channels/729741769192767510/795089627089862656/823887913955360818 Deleted User#0000: we are building a few niche products (not directly ML related, but uses some ML in the background). we're operating on a long-ish time horizon so there won't be anything interesting soon Louis#0144: welcome to the club Louis#0144: we're writing a vector analogy paper Louis#0144: well we should be Louis#0144: I kinda got burned out on it Louis#0144: LOL Louis#0144: all the experiments are done though Louis#0144: idk why everytime i open that overleaf Louis#0144: my brain kinda shuts down EricHallahan#1051: If you are interested in CLIP vector analogies, definitely pop into #art. Louis#0144: that too Louis#0144: @alstroemeria313 et al (including me) made a great notebook Louis#0144: it is pinned in art Deleted User#0000: nice! I will take a look triggerhappygandi#0001: Also yeah layernorm is pog. Deleted User#0000: we worked on this a bit back in 2014, using a BoW text encoder and image retrieval from pre-trained feature vectors. nothing really came out of it though. so CLIP got me excited about this again https://cdn.discordapp.com/attachments/729741769738158194/863076485057216532/Screen_Shot_2021-07-09_at_6.10.44_PM.png triggerhappygandi#0001: Quite impressive given it's pre-transformers :berk:
Louis#0144: @Deleted User if we ever want to suffer through another analogies paper Louis#0144: I’ll let you know Louis#0144: LOL triggerhappygandi#0001: Cat in the box is awesome though Deleted User#0000: it usually only worked with a single, centered object in the image. anything more complex and it was finicky. image generation also didn't really work yet 🙂 generic#8192: hullo, I'm Brendan, a prof at NYU Tandon (but please don't assume this means I know anything). I'm working on using language models for code generation, particularly interested in generating intentionally buggy code and transforming code to add bugs. will probably be mostly lurking but thought I'd say hi Sid#2121: Welcome @generic ! Great to see lots of new interesting people joining and introducing themselves - where did you all find out about us? generic#8192: I'd heard about EleutherAI from following gwern for a while, but the writeup yesterday pushed me to finally join :) Deleted User#0000: also the writeup 🙂 I'm really impressed with what this group has done Louis#0144: :hap: Louis#0144: You’ll soon see our geese addiction glucinater21#0869: The write up as well, I actually already knew about you guys when I tried to finetune gpt-neo for a Hackathon a while back with Google colab but it didn’t work 😦 Sid#2121: I guess you saw this from the codex paper? https://cdn.discordapp.com/attachments/729741769738158194/863085983745966120/Screenshot_from_2021-07-09_17-54-38.png generic#8192: yep! I'm really curious about what influences that, exactly. one idea I'm interested in looking at is if there are adversarial triggers that can induce buggy code, similar to this paper https://arxiv.org/abs/1908.07125 StellaAthena#3530: We have some models trained specifically on Python code you should look at! They’re on HF here: https://huggingface.co/models?search=ghpy They’re not really released and are therefore undocumented but you can ping bmk for help using them generic#8192: oh this is excellent, thanks! StellaAthena#3530: @generic I’m also a bit in the computer security space, and if you’re interested in talking about malware production or malicious bugs I can introduce you to people with similar interests. bmk#1476: I need to get ghpy6B up again right after interviews
generic#8192: very interested! we have done a little bit of work on ML for malware but not using LMs so far Louis#0144: Interesting…. I’m working on an ERNIEv3 model (similar to T5 with just a few extra tasks) I think a fixing corrupted code pretraining task could fit well into a T5 model or a BART model Louis#0144: @𓅬 gabriel_syme 𓅬 is generating room layouts for me to fix Louis#0144: Lol Louis#0144: Like architectural stuff generic#8192: yeah I've been wondering whether GPT models might not be a great fit for code since they only do forward-prediction Louis#0144: No I think Bart is better for what you want to do tbh Louis#0144: I’m scaling Bart right now Louis#0144: Well Louis#0144: I’m debugging our Bart code Louis#0144: lol generic#8192: many such cases ;) Louis#0144: https://github.com/morganmcg1/rotobart Louis#0144: Now obviously GPT J would walk all over a 1b parameter Bart Louis#0144: So in theory Bart would be better Louis#0144: But in practice I doubt it generic#8192: right, makes sense generic#8192: I've been clinging to my 774M param C/C++ GPT2 model because it took a month to train but at some point I'll want to start trying to train bigger versions Louis#0144: Jax Louis#0144: Use TRC
Louis#0144: Also doesnt tandon have its own super computer generic#8192: this was on NYU's cluster! 4xRTX8000s. but it's hard to reserve them for very long, too many other people doing ML :) generic#8192: I also have a 2x3090 system at home that I can use for smaller experiments but it gets a bit loud StellaAthena#3530: I’m bad at GPUs… what’s the equivalent computational power in A100s or V100s? Louis#0144: Oh wow generic#8192: the RTX8000s are pretty close to the V100s generic#8192: same RAM, similar speed EricHallahan#1051: Can we just drop this "V100/A100 days" business? It is a terrible unit IMO. EricHallahan#1051: I know it is useful, but still. StellaAthena#3530: IDK, what would be a better unit in your mind Manny96#3437: Funny, you mention that - this morning I produced an essay and GIT for Transhumanism, here in Australia; and mentioned Eleuther.ai. Manny96#3437: Would have liked to talk about it more, though EricHallahan#1051: FLOP? EricHallahan#1051: ¯\_(ツ)_/¯ StellaAthena#3530: I use it because I work with those GPUs and hours/days/months is an easily tractable unit of measure for me Manny96#3437: PFLOP StellaAthena#3530: My issue is that I don’t know what a PFLOP is StellaAthena#3530: Maybe that’s on me tho EricHallahan#1051: I am not saying it isn't useful, I am saying that it shouldn't be used in papers as the only unit of measure. StellaAthena#3530: Oh yeah. I wouldn’t use A100-days in a paper unless I was literally talking about how I ran 48 A100s for 10 days
generic#8192: I had some benchmarks of training GPT2 models with the 3090s as well as the RTX8000s if you want to calibrate: https://github.com/huggingface/transformers/issues/9371 StellaAthena#3530: Thanks Manny96#3437: https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/jax/mcmc/SliceSampler?hl=he - distilation? Manny96#3437: Is source utilising tfp? StellaAthena#3530: @Manny96 FYI you linked to the Hebrew page StellaAthena#3530: Or, at least it’s partially in Hebrew? Manny96#3437: Not in my end StellaAthena#3530: Weird, I see this https://cdn.discordapp.com/attachments/729741769738158194/863091269013864478/image0.png StellaAthena#3530: The bulk of the text is in English, but the website framing it is in Hebrew Manny96#3437: Oh, wait it is, lol Louis#0144: My dad just ate an entire watermelon in one sitting Louis#0144: Wtf Louis#0144: O thought this was off topic generic#8192: it's the `?hl=he` at the end Louis#0144: can someone translate some chinese for me for the ernie project Louis#0144: pls Louis#0144: its just a paragraph Louis#0144: apparently its middle chinese Louis#0144: so google translate is shitting itself Louis#0144: novel generation and couplet generation https://cdn.discordapp.com/attachments/729741769738158194/863102344799453214/Screen_Shot_2021-07-09_at_1.00.26_PM.png
Louis#0144: if anyone has a chance aze#1010: was this https://github.com/EleutherAI/github-downloader used in the pile? StellaAthena#3530: yes aze#1010: would fine tuning gpt-j on data from this ^ be dumb in that case Sid#2121: @bmk already did it iirc StellaAthena#3530: I thought he did that from scratch aze#1010: how are the results kurumuz#5695: im really curious about this aswell generic#8192: has anyone tried using distillation in "reverse" to get better initialization for training a larger model, assuming one already has a smaller model? generic#8192: I found some refs on layer-wise pretraining which is similar but seems to assume you're incrementally adding layers rather than increasing parameters at each layer as well Sid#2121: hm, like interpolating the weights of a smaller model to the size of a larger one as initialization? inox#5400: yes there is definitely a paper like this and I'm trying hard to remember the name, they design a network architecture that can be progressively grown while keeping the output the same at every growth step generic#8192: something like that, yeah - or something more sophisticated like trying to transfer gradients from the smaller model to the larger (?? somehow ??) generic#8192: I found this Bengio paper from Neurips which seems relevant https://papers.nips.cc/paper/2006/file/5da713a690c067105aeb2fae32403405-Paper.pdf bmk#1476: about like 2 years ago I tried doing model surgery by expanding all of the layers of the model with identity inits bmk#1476: it didn't work very well and I'm not sure if that was because it just doesn't work or because my implementation was borked bmk#1476: this was with tensorflow so the code was horrifying bmk#1476: but I managed to get a smaller model resumed as a bigger model + padding generic#8192: hmm I can sort of imagine how to do that by surgery yeah. I've played around some with directly tweaking weights in GPT2 (I made a "brain damage" script that progressively mutated more and more weights with gaussian noise and then sampled the outputs); and it's not too difficult Sid#2121: like just zero padding? did you try interpolation / tiling?
bmk#1476: I don't really remember this was way too long ago bmk#1476: but I remember being disappointed and giving up lol bmk#1476: what would I interpolate with? bmk#1476: also I'm pretty sure it was consecutive gpt2 sizes - might have been 117M -> 345M, so I couldn't really tile it generic#8192: hmm, maybe new weights would be average of 2-3 randomly selected weights from that layer in the old network generic#8192: ? generic#8192: (I have no principled reason to think that would be good) bmk#1476: why would I do that? generic#8192: I guess the idea is that just something numerically close to the weights of the original network might be better than random init. as I said though, I don't have any principled reason to think so inox#5400: I found it! https://arxiv.org/abs/1511.05641 generic#8192: nice, thanks! kindiana#1016: Zero padding would work if you are careful inox#5400: I wanted to combine this with SGDR back in 2016 and I'm not sure I ever did, like on every gradient schedule restart you change the network size kindiana#1016: It's like free rezero lol chilli#5665: hmm chilli#5665: there was a paper kinda like this chilli#5665: where they augmented a new part of the network chilli#5665: and froze the old part chilli#5665: and trained on a new task inox#5400: not sure how architecture search should work with transformers/MLP-mixer-likes now
inox#5400: on the one hand: it probably still works a bit, go for it! on the other hand: it's useful that these architectures are simple and generic dms#2699: n00b Q of the day: can anyone point me to some tips for handling large inputs with seq2seq transformers given token limits? I'm trying to chunk the input but coherence goes the way of the dodo dms#2699: best thing I've found is this but it seems to be a WIP https://www.machinecurve.com/index.php/2021/03/12/transformers-for-long-text-code-examples-with-longformer/ Louis#0144: Transformer xl is the only way I see rn Louis#0144: (Most) linear attentions are really bad Louis#0144: :/ Louis#0144: If you wanna implement transformer xl in Jax let me know Louis#0144: :^) Louis#0144: I’ve been hoping to avoid having to do it myself Louis#0144: Looks like a nightmare Louis#0144: :berk: Louis#0144: I kid I kid Louis#0144: If you wanna do it I’d be down to help Louis#0144: I need it for Ernie anyway Louis#0144: I’ve got like two months free before classes start dms#2699: I set up the project with pytorch but if jax is the way to go so be it 🦾 Louis#0144: Oh uh Louis#0144: Pytorch is much easier Louis#0144: Jax is hard for txl because recurrence is hard with fixed computation graphs
Louis#0144: @kindiana is it not? Louis#0144: People discussed this here before I thought Louis#0144: s2g atleast react with geese Louis#0144: LOL EricHallahan#1051: Local attention lol Louis#0144: He linked long former Louis#0144: I assumed he was looking into linear Louis#0144: 🤷‍♂️ Louis#0144: I’ll just go now I feel like I’m saying something that’s not technically correct Louis#0144: :berk: someKindaBean#8471: What's wrong with LongFormer? Longformer Encoder-Decoder should be able to do long seq2seq stuff Louis#0144: As in generation? Louis#0144: Isn’t longformer masked? Louis#0144: https://sshleifer.github.io/blog_v2/ Louis#0144: Ok who tf named their blog tensorgoose and why didn’t I think of this Louis#0144: @sshleifer found you Louis#0144: I like your blog Louis#0144: It’s so funny that like almost any ML scientist I run into is probably in this server txizzle#6710: hey folks, really cool 1 year retrospective! and awesome work. do you guys work on any RL here? Lord Parfington#0012: i'm so happy this thing was invented.
Louis#0144: #deleted-channel is basically the learning to summarize stuff Louis#0144: If that interests you Louis#0144: Im not sure if there’s any openings on that project though (?) Louis#0144: cc @Daj Louis#0144: I want to do hierarchical RL but my project #carp isn’t there yet Louis#0144: Maybe in a few months Louis#0144: Idk Lord Parfington#0012: are there any visual transformers that can accurately show written words and have been exposed to actual scripts and things? txizzle#6710: ah ok thanks, i will eavesdrop on those channels someKindaBean#8471: i just meant for summarization and yeah, it's sliding window attention Louis#0144: RL + language models is never a fun time bmk#1476: :ptsd: bmk#1476: also RL ~~+ language models~~ is never a fun time bmk#1476: ftfy bmk#1476: every experience ive had with RL is :ptsd: EricHallahan#1051: Friendship ended with RL, sequence modeling is my best friend. bmk#1476: granted most of tha involves LMs but Louis#0144: @bmk what’s worse
Louis#0144: Mesh tensorflow Louis#0144: Or RL EricHallahan#1051: RL in Mesh Tensorflow Louis#0144: That’s actually what kinda confuses me tbh Louis#0144: Why didn’t we use Jax in the very beginning Louis#0144: Who’s idea what’s it to do mesh TF Louis#0144: I still remember when I first joined Louis#0144: And I spent like txizzle#6710: so... trajectory transformer or decision transformer? 🙂 Louis#0144: A few hours helping Leo debug some random topology thing Louis#0144: Decision transformer txizzle#6710: call me sutton-pilled but TD is GOAT StellaAthena#3530: TPU Jax didn't exist at the time, at least not publicly Louis#0144: Oh ok cfoster0#4356: Lol two different meanings of Sutton-pilling clash EricHallahan#1051: https://blog.eleuther.ai/year-one Louis#0144: O yeah Louis#0144: I remember when we trained mesh tensorflow there were tons of issues with efficiency Louis#0144: Does Jax actually solve any of these Louis#0144: Or nah
Louis#0144: Like we couldn’t get above 50% efficiency or something (?) glucinater21#0869: What is a good Jax ml framework to try? I saw gpt-J used elegy Daj#7482: We are currently doing RL with LMs yes. We're...actually surprised how well it's working atm Daj#7482: but we don't expect RL to long term be the most stable method, so we're interested in testing a lot of other stuff Daj#7482: RL seems to maybe actually be not so bad as long as your implementation isn't horribly broken (most on github are) and your model is large enough :morelayers: Daj#7482: but I still expect sequence modelling to be better kurumuz#5695: o, interesting Daj#7482: Yeah but there are just like 1000 small subtle things that can completely break it Daj#7482: Which is why I'm happy we decided to implement from scratch lol Daj#7482: Instead of relying on broken public repos kurumuz#5695: we can build a RL pipeline i guess kurumuz#5695: messing with hidden state AR stuff rn though kurumuz#5695: too much fun Daj#7482: Yeah, what are you doing with that? That's also something I've worked on kurumuz#5695: Calculating distance between sequences to figure out which sequence is related to the last sequence that is submitted kurumuz#5695: to build a long term memory system kurumuz#5695: should be able to use it for classification aswell Daj#7482: Oh that idea aero had? kurumuz#5695: it is aero lol kurumuz#5695: it works pretty well
Daj#7482: Yeah kurumuz#5695: he works with us Daj#7482: Neat, I was hoping someone would try that Daj#7482: Seems so simple in retrospect kurumuz#5695: Didn't implement to the novelai yet, it is on sigurdbot kurumuz#5695: which people talks to in our discord kurumuz#5695: it seems to be getting grounded... kurumuz#5695: it has like 1 gig of these engrams/memories Daj#7482: Cool kurumuz#5695: getting pretty crazy, it was a really nice experiment kurumuz#5695: I want to do question answering with hidden states kurumuz#5695: not sure how that would work though kurumuz#5695: might not even need QA, but would be cool to get working :P Daj#7482: People finally seeing that hidden states are cool :berk: Daj#7482: And you don't need to use BERT for it kurumuz#5695: everything in one model Daj#7482: I wonder where the meme that AR states are bad came from kurumuz#5695: i kinda noticed MLMs are overrated Daj#7482: Exactly Daj#7482: We found the same
kurumuz#5695: casual masking should learn better world models kurumuz#5695: there should be a reason why most of the MLM parameters can be pruned and it keeps the same performance Daj#7482: Yep kurumuz#5695: i feel like ARs have an interface problem kurumuz#5695: its not they being incapable Daj#7482: That's what I've been banging on about for a while lol kurumuz#5695: we just dont interface correctly Daj#7482: Exactly kurumuz#5695: I am completely AR scalepilled rn lol kurumuz#5695: its good Daj#7482: Welcome to the party :berk: kurumuz#5695: who else is in the party? kurumuz#5695: is it just us :berk: Daj#7482: Eleuther, OpenAI... Daj#7482: uhh Daj#7482: maybe Cohere? kurumuz#5695: o cool Daj#7482: I'm still surprised more people haven't caught on kurumuz#5695: yeah kurumuz#5695: kinda crazy
kurumuz#5695: so much potential Daj#7482: As the saying goes, to get ahead, you don't have to predict the future, just realize the present kurumuz#5695: it took me a while to do that tbh EricHallahan#1051: Those are all I can think of. ¯\_(ツ)_/¯ EricHallahan#1051: Google? EricHallahan#1051: I don't think so. kurumuz#5695: they like T5 and bert though kurumuz#5695: lol Daj#7482: Yeah it goes against a lot of instincts, glad we finally bullied you enough :berk: Daj#7482: Google is not one monolith. Google Brain is scale pilled Daj#7482: They have Noam kurumuz#5695: well i kinda knew it, but took some time to admit it and change things kurumuz#5695: I was always an end to end fan otherwise kurumuz#5695: lol Louis#0144: Interesting! Louis#0144: Very surprised kurumuz#5695: feature engineering is a fool's errand tbh, i might be fine with end to end KGs Daj#7482: same tbh. It's not like it's working great, small models and stuff atm kurumuz#5695: not feature engineering though Daj#7482: goose BTFO
kurumuz#5695: just learn it all kurumuz#5695: lol Daj#7482: :berk: Daj#7482: Welcome to the future kurumuz#5695: elon is end to end pilled too btw kurumuz#5695: they're gonna crush waymo so good Daj#7482: You mean Karpathy kurumuz#5695: elon had tweets about it kurumuz#5695: switching from hand coded planning to end to end learning Daj#7482: Sure, but it's Karpathy doing it Daj#7482: And Karpathy is :bigbrain: bmk#1476: ~~if GB is scalepilled then why do they keep training useless MoEs~~ kurumuz#5695: oh yeah ofc kurumuz#5695: he is big brain bmk#1476: Elon tweets about everything tho guac#4716: sad hinton noises Daj#7482: ~~they took the off-brand scale pill~~ kurumuz#5695: i think elon personally understands end to end is the way to go kurumuz#5695: well i think he learned it kurumuz#5695: he spoke about spending time on hd mapping and feature engineering and that being a waste of time
kurumuz#5695: and changing direction after that bmk#1476: in any event I don't think Musk being interested in something provides much bayesian evidence for something being a good idea, for anyone with a ratpilled prior Daj#7482: Controversial opinion: I think Elon being interested in something is a much stronger signal than baseline Louis#0144: Tbf I am doing Ernie now which is somehow both KGs and end to end kurumuz#5695: I agree kurumuz#5695: I love elon tbh Louis#0144: Which I love to no end bmk#1476: baseline means normie or rat? kurumuz#5695: KGs are corrupting that model kurumuz#5695: i swear kurumuz#5695: LMAO Daj#7482: Normie up to and including Very Smart Normie (prestigious professor or the like) bmk#1476: oh kurumuz#5695: I am pretty convinced MLM head of that model is making it worse bmk#1476: ok I agree with that bmk#1476: but I think when compared to rats, he doesn't provide much signal Daj#7482: also better than average rat, but not average "well known" rat kurumuz#5695: the fuck is a rat Daj#7482: rationalist lol kurumuz#5695: what are you guys hiding
Daj#7482: LessWrong poster kurumuz#5695: ohhh bmk#1476: I trust gwern's opinions 10x more than musk Daj#7482: I mean, duh Louis#0144: It actually has a MLM head similar to BART where it can just fill in any amount of text in place Daj#7482: ~~though not on _all_ topics~~ Louis#0144: It’s not like BERT kurumuz#5695: hmmm Louis#0144: It’s some AR + MLM hybrid kurumuz#5695: based bmk#1476: musk likes anime too Daj#7482: I'm not saying I trust Musk on that either kurumuz#5695: i just dont think having a MLM head contributes much to the model, they think it got better because they have a good interface for NLU which is MLM kurumuz#5695: I don't think it learns any better world models Daj#7482: Seems likely, but I do wonder if an MLM head might be a useful feature on top of a strong world model 🤔 Daj#7482: Never thought about that Daj#7482: not sure, might also fuck things up Daj#7482: Since an AR model needs to really learn a strong causal model, and MLM breaks causality in a way Daj#7482: maybe not idk kurumuz#5695: i think it might corrupt it
kurumuz#5695: its my concern kurumuz#5695: do you know what you can do though? Louis#0144: I’ll run ablations Louis#0144: lol Louis#0144: I’m not worried Teemochu#8740: Elon being interested in something is a very strong signal that it has a potential to be both revolutionary and marketable IMO. Teemochu#8740: "interested" here being a tighter definition than the casual one bmk#1476: only if your prior is normie Teemochu#8740: "This is a rock I haven't looked under, and he is looking under it" is a decent sign to me that I should at least look under that rock. kurumuz#5695: in elon we trust Teemochu#8740: in a similar way that seeing a slightly off-the-beaten-path place/game/etc recommended to me by someone whose recommendations tend to be good is a pretty strong sign I should at the very least put it on my to-someday list mgostIH#0245: bruh, mars txizzle#6710: newb question, what is MLM / AR? masked LM and autoregressive? cfoster0#4356: Yes txizzle#6710: thanks txizzle#6710: just an e2e RL lurker dont mind me 😄 joaogui1#8461: Very much joaogui1#8461: Why Decision and not Trajectory? Louis#0144: 🤷‍♂️ I really like decision tbh Louis#0144: It just feels more natural
Louis#0144: Also I haven’t had too much time to read into trajectory in detail Louis#0144: But I’ve played with trajectory sequence modeling before Louis#0144: Back in 2019 Louis#0144: And it was weird Louis#0144: Especially in transformers Louis#0144: I was using XLnet joaogui1#8461: Huuum kurumuz#5695: hmmm zphang#7252: lol why people hating on MLM bmk#1476: My Little Model kindiana#1016: Everyone is sick of masks nowadays cfoster0#4356: Causal mask: :guilty: kurumuz#5695: to be edgy ofc kurumuz#5695: :berk: kurumuz#5695: jk Louis#0144: I love MLM chilli#5665: hmm bmk#1476: where's tha image of a facial mask with the word [MASK] chilli#5665: What's the current consensus on MLM vs AR? Louis#0144: MLM is inherently harder to do right I feel
Louis#0144: So it has to catch up to AR chilli#5665: MLM is better for understanding tasks? Louis#0144: Yeah cfoster0#4356: Consensus? Idk if there is one chilli#5665: AR is better for genration? Louis#0144: Personally I prefer masking the way BART does it Louis#0144: 🤷‍♂️ kurumuz#5695: AR is good at NLU too kurumuz#5695: we use it for NLU Louis#0144: You use Bart Louis#0144: Lol kurumuz#5695: no kurumuz#5695: hidden engrams are NLU Louis#0144: Ohhhh Louis#0144: Yeah I’d agree with that cfoster0#4356: Feel like some folks are thinking about their MLM sunk costs Louis#0144: Sure Louis#0144: For sure zphang#7252: I'm not caught up on ERNIE, but deberta matched T5 performance on SuperGLUE with a tenth the parameters, and the LMs weren't even in the top contenders kurumuz#5695: i feel like NLU problem for AR is not that model is not capable, but we don't interface with AR models correctly
kurumuz#5695: so it's an interface problem joaogui1#8461: ? Louis#0144: He’s implying retrieval is an NLU task Louis#0144: Which I am inclined to agree with kurumuz#5695: yes zphang#7252: I think that's a plausible hypothesis, but not one that's confirmed yet joaogui1#8461: Oh, got it joaogui1#8461: MLM generates better representations tho joaogui1#8461: Which is good for stuff like clustering kurumuz#5695: how so chilli#5665: This is the part that people are discussing chilli#5665: and disagreeing on Louis#0144: I don’t know if people agree with this in general @joaogui1 kurumuz#5695: I would say casual masked models learn better world models Louis#0144: I had a lot of success doing retrieval with AR models in February joaogui1#8461: NLU is not the same as embeddings Louis#0144: When DPR was not performing well joaogui1#8461: I mean the Sentence-Transformer folks seem to think so Louis#0144: Sentence transformer is so good tbh Louis#0144: And DeCLUTR
joaogui1#8461: And GPT embeddings are like super pathological Louis#0144: it depends what layer you take the embedding from Louis#0144: Last layer embeddings kinda suck Louis#0144: I had more success on second to last Louis#0144: Back when I tried doing visual grounding stuff cfoster0#4356: Using the embeddings straight out the box shouldn't work well, right? Louis#0144: Yeah joaogui1#8461: Same for BERT though Louis#0144: That too cfoster0#4356: There's a huge task mismatch cfoster0#4356: BERT etc happen to have a prefix task that's closer joaogui1#8461: But BERT's work better than GPT Louis#0144: It always surprises me how well Bert held up Louis#0144: All these years later Louis#0144: Vanilla Bert is still really good joaogui1#8461: Yeah joaogui1#8461: Just remove NSP Louis#0144: Mhm chilli#5665: I agree that the common consensus is that MLM generates better embeddings zphang#7252: there's no reason to use BERT over RoBERTa in that case lol
chilli#5665: but I think there's a lot of people who don't believe that should be the case chilli#5665: and there've been several papers that do some small tweak/fine tuning on AR models to have them match MLM models for these kind of embedding based tasks cfoster0#4356: I've heard this anecdotally but can't easily recall any apples to apples comparisons zphang#7252: if you're doing anything that's token-wise, LMs already come out of the gate with a huge disadvantage joaogui1#8461: We did some SentEval benchmarking at cohere and the difference is pretty big cfoster0#4356: Yeah, and as kuru mentioned, that creates an interface mismatch joaogui1#8461: https://paperswithcode.com/paper/isotropy-in-the-contextual-embedding-space chilli#5665: I think this is a common belief chilli#5665: or you can just look at the leaderboards for most of these NLU leaderboards, which are mostly still dominated by non-AR models joaogui1#8461: The difference between Roberta and Bert is kind of weird really, it's only a Bert that you're training more and without NSP cfoster0#4356: What representation did you take, out of curiosity? chilli#5665: https://arxiv.org/abs/2103.10385 joaogui1#8461: I believe just last layer chilli#5665: This is one of the papers that's trying to demonstrate that AR is comparable to MLM in terms of embeddings joaogui1#8461: Again embeddings ≠ NLU cfoster0#4356: Like, which token(s)? joaogui1#8461: Average of last layer zphang#7252: yea, but it's a drop-in, almost dominant replacement. Nothing comes to any mind for any case where BERT is better than RoBERTa, so I wonder why people still use plain BERT lol chilli#5665: how so cfoster0#4356: Oh that'll be an issue
cfoster0#4356: Imo cfoster0#4356: Because information can't propagate properly chilli#5665: for the most part, NLU is reliant on embedding quality joaogui1#8461: Oh sorry, I just meant that sometimes people say BERT but they're using Roberta chilli#5665: in the typical pretrained model -> fine tuning setup joaogui1#8461: It's just the difference is weird joaogui1#8461: We don't rename ResNets just because we train them for longer or something like that joaogui1#8461: Because when I say embeddings I mean what you can do with the embeddings in an unsupervised way joaogui1#8461: So no fine-tuning allowed zphang#7252: ah I see. no that's fair joaogui1#8461: For example clustering zphang#7252: The GPT Understands,Too paper does have some issues, but it's headed in the right direction of "if we properly exploit LMs, we can get much closer to MLM performance in these task formats" chilli#5665: when I talk about embeddings I'm talking about their usefulness for downstream tasks joaogui1#8461: Again just look the at Sentence-Transformers examples zphang#7252: That said, I think it's premature to call MLM old and busted kurumuz#5695: I can setup a classificaton pipeline on GPT-J 6B soon and can compare that to BERT with some prompt tuning zphang#7252: why prompt tuning though? kurumuz#5695: BERT gets finetuned for specific tasks kurumuz#5695: why shouldnt GPT chilli#5665: well, i think another reason people don't like MLM is that it's a lot .... hackier than AR
joaogui1#8461: In fact they even found that the correlation between GLUE/SuperGLUE and out of the box embeddings performance is surprisingly low zphang#7252: Right, why not full fine-tuning? kurumuz#5695: oh, dont think its necessary chilli#5665: Like, AR is a lot more fundamental than MLM zphang#7252: lol I would say that's unfairly disadvantaging the MLM models kurumuz#5695: how so kurumuz#5695: I can do a finetune sure, if that is a concern zphang#7252: I guess it depends on what claim we're trying to test. The current and common use-case for MLM models is fine-tuning the whole thing joaogui1#8461: You're going to compare 6B GPT with 300M Bert? kurumuz#5695: deberta kurumuz#5695: and yes zphang#7252: still 1.5 to 6 though kurumuz#5695: we can do gpt-neo i guess kurumuz#5695: if that is fine Sphinx#2092: You could compare finetuning only the t5 encoder Sphinx#2092: That could be fun. zphang#7252: the fairest would be GPT-2 vs. DeBERTa, no? kurumuz#5695: GPT-2 is awful joaogui1#8461: Yeah that's fairer
kurumuz#5695: deberta is trained good kurumuz#5695: gpt-2 is not joaogui1#8461: And then you should do full fine-tuning zphang#7252: is Neo-1.5B better than GPT-2? kurumuz#5695: yes kurumuz#5695: absolutely zphang#7252: which metrics joaogui1#8461: Or you could do Neo-1.5B fine-tuning Vs DeBERTa fine-tuning vs GPT-J prompt-tuning kurumuz#5695: lol just me playing with it for storytelling purposes joaogui1#8461: That would be interesting kurumuz#5695: can run some evals ersatz#0001: It’s actually very useful for many things kurumuz#5695: GPT-J would probably destroy both lol zphang#7252: oh, generation I wouldn't know, but for NLU tasks I wouldn't be surprised if the Pile diversity actually hurts it somewhat joaogui1#8461: But again, this is all testing for NLU, not the same as embeddings kurumuz#5695: might be wrong, pretty wild speculation kurumuz#5695: sorry joaogui1#8461: Depends joaogui1#8461: What do you mean by prompt-tuning? zphang#7252: anyway, my bet would be that GPT-Neo-1.5 or GPT-2 would underperform DeBERTa on NLU tasks under the fully fine-tuned format, but I would be interested if I'm proven wrong!
kurumuz#5695: https://arxiv.org/abs/2104.08691 joaogui1#8461: If it's this paper here zphang#7252: (would be happy to collaborate on such an experiment lol) kurumuz#5695: no, prompt tuning is really cool kurumuz#5695: we deployed it in novelai kurumuz#5695: people love it kurumuz#5695: really effective joaogui1#8461: Huuum, cool kurumuz#5695: better than we imagined tbh chilli#5665: how expensive is prompt tuning? kurumuz#5695: yeah sure, will let you know if i really do it chilli#5665: pretty cheap? kurumuz#5695: pretty cheap yeah. cfoster0#4356: It's a very smart usage of causal attention models, ngl zphang#7252: let me know if I can help! kurumuz#5695: we will let users do their own prompt tunes joaogui1#8461: Also DeBERTa will be normal fine-tuning right? Not SIFT kurumuz#5695: I can do normal finetunes on all models. zphang#7252: SIFT is just a slightly more robust fine-tuning method, I don't think it makes much of a difference unless you're clawing up the leaderboard cfoster0#4356: My recommendation would be to take the embedding of the last token, for the GPT models
joaogui1#8461: I think it makes a big difference to be honest zphang#7252: I think there are several ways you can approach it actually kurumuz#5695: would be nice to have a 6B deberta... joaogui1#8461: Like to me it's the reason DeBERTs beats a model 8 or so times bigger (T5) zphang#7252: you can take the "single token representation format" which I think advantages the MLM models zphang#7252: or you can take the "use any weird task format you want" which I think will give the LM models a (fair) boost zphang#7252: but we should have both chilli#5665: what's the largest MLM model? chilli#5665: T5? Sid#2121: probably, maybe even ByT5 Sid#2121: can't remember the largest T5 at this point Sphinx#2092: They are seq2seq models, so it's not even a fair comparison. zphang#7252: 11B was the largest, unless there have been larger since kurumuz#5695: yeah should be 11b Sphinx#2092: mT5 XXL is 13. Sphinx#2092: dat vocab. zphang#7252: such multilingual vocab zphang#7252: time for the 2021 NLP-lympics! Sphinx#2092: Either way, seems like the comparison should be between encoder-only models. chilli#5665: hmm
joaogui1#8461: For context: the second citation when they describe SIFT is from another MS paper that uses pretty much the same method to get a bert-large to pretty much the same performance as T5-3B IIRC chilli#5665: what's the largest encoder-only model then? Sphinx#2092: I wouldn't be surprised if shit like Rembert would crush some of thse things. Sphinx#2092: Though I would also like to see people finetuning encoders from seq2seq models. chilli#5665: akronomicon says megatron-BERT? Sid#2121: what's akronomicon chilli#5665: https://lair.lighton.ai/akronomicon/ joaogui1#8461: Serious is DeBERTa, Megatron is 8B though zphang#7252: you mean SMART? joaogui1#8461: Are the Megatron models public? Sid#2121: damn this is cool lol chilli#5665: Megatron-BERT is 3.9B according to akronomicon joaogui1#8461: Yeah, that one joaogui1#8461: My bad chilli#5665: Are you referring to the Megatron-LM (decoder model?) Sid#2121: largest megatron model is ~11B model trained by FAIR - but it sucks and it's AR chilli#5665: also, it's not on the leaderboard Sid#2121: because no one can get it working lmao joaogui1#8461: Yeah, that's way I don't count it as serious haha joaogui1#8461: So for serious models it's DeBERTa
Sid#2121: :tribalism: https://cdn.discordapp.com/attachments/729741769738158194/863201434493648936/Screenshot_from_2021-07-10_01-33-58.png chilli#5665: Sid's referring to the Megatron-11B model (from FAIR), which is different from the Megatron-8B (from Nvidia). chilli#5665: And these are all decoder models chilli#5665: AFAIK, Megatron-8B works fine Sid#2121: i don't think nvidia ever released megatron-8b though? chilli#5665: oh really? Sid#2121: afaik the biggest model released by the megatron team is 300M, but i'd love to be proven wrong EricHallahan#1051: They didn't release anything IIRC. joaogui1#8461: And I'm referring to the 3.9B Bert in Megatron which wasn't released EricHallahan#1051: Or anything large enough to matter at least. chilli#5665: ? chilli#5665: well, except the code Sid#2121: Pinned a message. Sid#2121: pinning that so i remember it :berk: chilli#5665: yeah it's quite useful kurumuz#5695: based enough if you ask me chilli#5665: yeah the code's been useful for a lot of people kurumuz#5695: pangu-a is crazy lol joaogui1#8461: You asked about the largest encoder model chilli#5665: and everybody calls model-parallelism on transformers "megatron-LM style parallelism"
joaogui1#8461: Yeah kurumuz#5695: i call it model sharding :berk: chilli#5665: https://cdn.discordapp.com/attachments/729741769738158194/863202272518340628/unknown.png zphang#7252: Hmm, I think the picture is a lot more mixed. If I'm looking at the results right, it's a combination of SMART and MT-DNN, and the way they slice the results makes it hard to parse where SMART helps. Where they do have direct comparisons, SMART usually adds a couple points, with larger improvements on adversarial tasks Also I think they have a newer SIFT lol chilli#5665: I was just referring to this chilli#5665: there's a megatron-11B that's released (that sucks) from FAIR bmk#1476: why is it US flag lol chilli#5665: and a megatron-LM-8B that isn't released from Nvidia chilli#5665: and then a megatron-BERT-3B that also isn't released chilli#5665: lol chilli#5665: what annoying naming schemes CRG#8707: English text? cfoster0#4356: SIFT isn't specific to MLM/encoder models, right? joaogui1#8461: Nope joaogui1#8461: It's barely specific to NLP guac#4716: `us-central-1` bmk#1476: angry "US != english" noises kurumuz#5695: was eating my popcorn when people were using 11b on their product just because the number beeg
kurumuz#5695: :ultraberk: bmk#1476: actually our models are trained in euw4a chilli#5665: hmm Louis#0144: @chilli what’s your thoughts on Ernie gen chilli#5665: I feel like models should be sorted by Flops tbh Louis#0144: https://arxiv.org/abs/2001.11314 joaogui1#8461: A newer SIFT, damn chilli#5665: haven't read it chilli#5665: should ask somebody else 🙂 Sid#2121: @chilli is torch ever gonna get pmap Louis#0144: True kurumuz#5695: what is the difference between erniev3 Louis#0144: Ernie gen is something you add to T5 or Ernie to improve generation quality chilli#5665: depends on what you mean by pmap Louis#0144: You add it as a finetune step I think Louis#0144: No one has scaled it tho Louis#0144: So I have no idea how it does Sid#2121: I want to remove all the megatron mp primitives and just do :chad: wrapping my model code in pmap like in mtj Louis#0144: There’s like 20 Ernie papers that all seem promising but no one has combined or scaled them yet Louis#0144: Rip
joaogui1#8461: So we can eventually get Erniev3-15B-Gen kurumuz#5695: that would be beautiful chilli#5665: the main bottleneck is having an XLA-equivalent that does the MP primitives I think joaogui1#8461: That's xmap not pmap kurumuz#5695: @finetune was mad he couldnt print() the weights though chilli#5665: xmap is basically just a wrapper above pmap chilli#5665: well, wrapper isn't really the right word chilli#5665: but like, another interface above pmap chilli#5665: As in, the actual operations performed by xmap can be duplicated by pmap + vmap Louis#0144: rotoERNIEv3-15B-GEN-ViL Louis#0144: lol kurumuz#5695: god Louis#0144: + one other thing kurumuz#5695: what is that name joaogui1#8461: Lol Louis#0144: I think ERNIE needs like Louis#0144: months of ablations Louis#0144: :berk: chilli#5665: there are some shardedtensor RFCs floating around if you're interested @Sid zphang#7252: just shorten it to ERNIE-ViL, or EViL for short
bmk#1476: still better than eleuther-alm-gpt-neo-mtf-pile400B-2.7B-v1.2-base-tuned-default-framework-system-library Deleted User#0000: I used pmap on GPUs and it worked perfectly Louis#0144: perfect TRC project chilli#5665: but I think it's something that's a work in progress Sid#2121: How about just not starting with a shit model in the first place :thonk: Louis#0144: ye Louis#0144: we are] Louis#0144: rotoBART Sid#2121: AR tribalism :tribalism2: Louis#0144: o Louis#0144: no I want to explore seq2seq Louis#0144: thats the purpose of this chilli#5665: data-parallel only or model-parallel too? Sid#2121: i am! kurumuz#5695: AR is all you need in a year Deleted User#0000: just data parallel Louis#0144: the entire purpose is to focus on seq2seq Deleted User#0000: Ben told me to just use pjit instead of xmap Louis#0144: could ERNIE be made autoregressive tho? Louis#0144: I dont entirely think so?
chilli#5665: I don't think pjit works on GPUs right now right? Sid#2121: dunno, still haven't read the paper joaogui1#8461: This is the 8th place in GLUE Microsoft D365 AI & MSR AI & GATECH MT-DNN-SMART kurumuz#5695: ofc kurumuz#5695: :berk: Deleted User#0000: Yeah, it would be for TPUs chilli#5665: @Sid https://github.com/pytorch/pytorch/issues/55207 Deleted User#0000: If the Jax devs solve it for GPUs zphang#7252: JAX devs plz Louis#0144: link? chilli#5665: I think it's mainly bottlenecked on XLA devs Deleted User#0000: That would be crazy Sid#2121: awesome, will be following closely Louis#0144: im not convinced bc the ERNIE architecture is entirely based around T5 and theyve invested a lot in improving gen quality Louis#0144: lol zphang#7252: oh that's just adding the team names though kurumuz#5695: i wish jax didnt have slow gpu inference kurumuz#5695: should investigate that zphang#7252: wait does it?
Louis#0144: no? Louis#0144: lol kurumuz#5695: does what Sid#2121: this would be fucking awesome https://cdn.discordapp.com/attachments/729741769738158194/863204542770446356/Screenshot_from_2021-07-10_01-46-24.png zphang#7252: have slow GPU inference kurumuz#5695: i compared it with huggingface pytorch Louis#0144: Its just bc Ben's code isnt optimized for inference I thought kurumuz#5695: it was much slower Louis#0144: lol kurumuz#5695: idk, its fast on TPUs kurumuz#5695: pretty fast joaogui1#8461: Just reading the leaderboard really: https://gluebenchmark.com/leaderboard joaogui1#8461: But there's an evil repo that barely runs with all of the code kurumuz#5695: are you serious zphang#7252: ya I would assume well written JAX / PyTorch code would basically run at the same speed on GPUs kurumuz#5695: god kurumuz#5695: GOD kurumuz#5695: please kurumuz#5695: imagine a future where sharding isnt pain chilli#5665: I feel like it isn't that bad with pjit/GSPMD
chilli#5665: :thonk: Louis#0144: this web page is actually just awful Louis#0144: LMAO Louis#0144: wtf Sid#2121: or, even nicer https://cdn.discordapp.com/attachments/729741769738158194/863204873221439508/Screenshot_from_2021-07-10_01-47-41.png Louis#0144: Its so laggy on mobile zphang#7252: I TA'ed for a class where one project was doing some experiments on SMART and they tried using the mt-dnn code base. In the first meeting I told them to just reimplement the SMART algo with HF/T as a base rather than touch mt-dnn lol joaogui1#8461: Yeeeah chilli#5665: One thing I wonder about for XLA code, is that if you're not happy with the performance after you jit your model chilli#5665: what do you do? joaogui1#8461: I've tried to use that, it's painful joaogui1#8461: Don't think I'll ever recover chilli#5665: maybe @joaogui1 has experience with this? joaogui1#8461: There's a bunch of stuff to check zphang#7252: ehh, I don't think it's the worst ML code base I've worked with... joaogui1#8461: Like if you're recompiling stuff joaogui1#8461: If you can't just jit more of your code Louis#0144: ERNIE ablations would be blog post worthy? or paper worthy chilli#5665: yeah, so let's say you're not doing stupid things like that Louis#0144: im thinking blogpost
chilli#5665: like, what do you do if you're not happy with how XLA compiled some subgraph Louis#0144: tbh Louis#0144: no reason to make it more than just a giant table joaogui1#8461: No idea tbf zphang#7252: write more papers, flood the short paper market Louis#0144: :berk: chilli#5665: hmm zphang#7252: monthly submissions to ARR Louis#0144: lul Sid#2121: I'm guessing there's not three separate compilers for XLA like there is for jit lol joaogui1#8461: @jekbradbury chilli#5665: I guess if you're on TPUs you don't really have a choice anyways chilli#5665: It's not like you're gonna write faster TPU code (or can you?) bmk#1476: oh shit emnlp author response is in like 2 days zphang#7252: what'd you submit bmk#1476: i get to know whether my short paper was total garbage! bmk#1476: the, uh, dataset filtering thing zphang#7252: oh right zphang#7252: resubmit pile with Neo + 6B results :thonk: jekbradbury#2280: internally: you file a bug and the XLA oncall looks at it
externally: one of us files the bug for you, at least for now zphang#7252: and add in all the random important sentences that we deleted bmk#1476: eh im not in a hurry to get pile published atm zphang#7252: like definitions bmk#1476: we should submit to a journal isntead bmk#1476: so we can get the full length chilli#5665: Has the XLA team considered exposing some more performance knobs? jekbradbury#2280: they expose a ton of knobs as flags, and we haven’t yet figured out how we’re going to expose those flags for OSS users chilli#5665: Is there documentation for those flags somewhere? chilli#5665: concretely, I've been benchmarking some ML compilers on some toy tasks, and I want to try to optimize XLA perf kurumuz#5695: huh, might be interesting for me too. inox#5400: submit to JMLR, the premier machine learning journal that runs on a potato in a grad student's office somewhere bmk#1476: honestly i might joaogui1#8461: I really like the JMLR inox#5400: same, unironically I love that potato inox#5400: who would win: JMLR with zero resources vs Nature inox#5400: JMLR every time jekbradbury#2280: each flag has a docstring in the code where it’s declared AI_WAIFU#2844: 😎 jmerizia#4039: Seems like it would take a long time to implement sharded tensors, but it seems like an elegant solution once it works. Pretty much every layer would have to change I think. Or you'd need separate sharded layers
jmerizia#4039: Sharded layers are a thing I'm working on in my research lab chilli#5665: From my understanding, that's basically how XLA works chilli#5665: except of course, with a far smaller set of primitives compared to PyTorch jmerizia#4039: Yea. For things like cnns, there would need to be a lot of communication still chilli#5665: well, yeah, that's why sharding is mainly only useful if you have fast interconnect jmerizia#4039: So I think it will be several months before we can pass a ShardedTensor into a Conv2d chilli#5665: want to elaborate on what you're working on? jmerizia#4039: https://github.com/distdl/distdl jmerizia#4039: It's a collection of parallel implementations of existing pytorch layers jmerizia#4039: (very early research software lol) chilli#5665: How are you defining your parallel tensors? jmerizia#4039: They are just normal tensors. The sharded tensor abstraction is nice, but it's expensive in terms of dev hours. Instead, the layers are given Partition objects, which carry info on how the input is sharded chilli#5665: yeah, but how do you actually shard your tensors across different devices? jmerizia#4039: Oh under the hood it's MPI (and soon NCCL) Spy#9778: @alstroemeria313 @𓅬 gabriel_syme 𓅬 This isn't super great but since you guys helped so much when I was getting this working I figured I should upload it: https://github.com/davisyoshida/vqgan-haiku/tree/master Spy#9778: Thanks again! Spy#9778: I had to unpack a bunch of stuff from my davis-utils package to put in the utils.py Spy#9778: and it was really boring Spy#9778: so instead I wrote a thing which takes files which import davis-utils and puts all the used functions/classes into a single utils file Spy#9778: it only took like a day to save 10 minutes 😎
kindiana#1016: Sharded conv does work on xla I believe kindiana#1016: It does a halo exchange jmerizia#4039: Oh that's interesting jmerizia#4039: Do you know if GPU is on the roadmap for the jax team? kindiana#1016: @jekbradbury jekbradbury#2280: yes, absolutely (in fact the sharding features like pjit and xmap already support GPUs, the missing thing is open source/documented support for multihost GPU) kurumuz#5695: I should get into JAX soon and see why it's slower compared to huggingface pytorch for GPT-J 6B. kurumuz#5695: if that is solved, really useful for model sharding chilli#5665: oh, pjit/GSPMD works on GPU now? nice jekbradbury#2280: yeah, just recently chilli#5665: cool, I was trying it out previously on a project and got some weird errors that made me unsure about whether it was supported 𓅬 gabriel_syme 𓅬#3220: You mean finetuning or inference? Louis#0144: To be clear Louis#0144: I think sharding would be slow for inference in general Louis#0144: If the model can already fit on a single card Louis#0144: No? kurumuz#5695: no kurumuz#5695: with a good interconnect linear speedup. kindiana#1016: to be more precise you need a low latency interconnect kindiana#1016: and parallel ff + attn also helps
kindiana#1016: lol Louis#0144: O yeah Louis#0144: True Louis#0144: lol Louis#0144: We literally discussed this kuru Louis#0144: Ur right kurumuz#5695: @Louis you remember the 3090 x2 benchmark? kurumuz#5695: it was linear 2x speedup jmerizia#4039: Was that with nvlink? kurumuz#5695: not sure, might be pcie4. jmerizia#4039: do you have the paper/website? kurumuz#5695: no Louis#0144: True kindiana#1016: I think theres also a way by modifying the model architecture slightly to totally make it not communications bound kindiana#1016: but I won't elaborate for now :berk: kurumuz#5695: decoder transformer? kurumuz#5695: wow kurumuz#5695: that is crazy kurumuz#5695: i will try to figure it out lol Louis#0144: Wut
Louis#0144: Wait elaborate @kindiana Louis#0144: I’m curious now kurumuz#5695: you gave me a giant hint kindiana#1016: :goose6: kurumuz#5695: that is good enough Louis#0144: I’ve been reading your code base so much lately LOL bmk#1476: is it local attention kindiana#1016: no bmk#1476: dang Louis#0144: Im trying to do T5 in your code base but the dual heads of T5 is making that hard Louis#0144: I’ll figure it out eventually Louis#0144: @kurumuz this isn’t a hint FYI Louis#0144: lol kindiana#1016: that drops half your model parallel communication kindiana#1016: but I think you can drop all of it 🤔 bmk#1476: only put attention in half of the layers :bigbrain: kindiana#1016: that doesn't reduce communication volume lol kurumuz#5695: louis, i implemented parallel ff + attn in neox kurumuz#5695: i know lol jmerizia#4039: put attention in sqrt(n) of the layers?
Louis#0144: Ok lol jekbradbury#2280: ok i’m intrigued kindiana#1016: alright I'll spill the beans due to popular demand :berk: instead of adding the output of a layer to the residual immediately, add it after the next layer jekbradbury#2280: transformer decoding is highly memory bandwidth bound, and the only way i know to avoid that is to make it communication bound instead kindiana#1016: that way MP communications is non blocking jekbradbury#2280: ah yeah, ok, it’s about overlap jekbradbury#2280: there are many ways to improve overlap 🙂 kindiana#1016: I'd be curious if GPT-J weights tolerate being run with late-residual-add kindiana#1016: maybe thats something you want to investigate @kurumuz lol kurumuz#5695: yeah definitely kurumuz#5695: after i get my sleep will try kindiana#1016: it also depends on pytorch/xla/whatever being smart enough to actually do the overlap kindiana#1016: I'm not sure if they are lol Louis#0144: What are others? jmerizia#4039: that's also interesting for interpretability Louis#0144: How jekbradbury#2280: mostly different kinds of op splitting; imagine writing a natively distributed matmul algorithm or something jekbradbury#2280: XLA has historically not been good at this, but it’s getting a lot better kurumuz#5695: XLA seems really promising
jmerizia#4039: such ablations are along the line of thinking about interpretability. i.e., can I cut something out and not get garbage. I think Gurkenglas will say something to this point tomorrow. But it's unrelated to performance (sorry to distract lol) Dupelet#9080: Just wondering - would any of the core team be interested in doing an AMA on Reddit about GPT-Neo? Dupelet#9080: I'm not quite sure who to reach out to to invite Louis#0144: @StellaAthena often answers questions on Reddit Louis#0144: But we recently found out she does not partake in gooseposting. Louis#0144: Don’t know if that matters for the AMA Louis#0144: 😉 EricHallahan#1051: We have been asked in the past, and we turned it down at the time. EricHallahan#1051: Though I don't know if things have changed. 𓅬 gabriel_syme 𓅬#3220: Is that a napkin space moment? :berk: 𓅬 gabriel_syme 𓅬#3220: Will that be available at some point? :) AI_WAIFU#2844: I bet it works, but I also bet there's a minimum amount of serial computation you have to do to avoid diminishing returns. kindiana#1016: well, currently its ~28 kindiana#1016: as opposed to 56 of a regular transformer of the same arch kindiana#1016: idk if 14 will work kindiana#1016: lol AI_WAIFU#2844: Eh, you can just up the batch size to compensate. AI_WAIFU#2844: More serial -> more time per iteration due to latency. More batch -> more examples for same latency kindiana#1016: well, not if you are latency bound for inference or something kindiana#1016: none of these are a huge deal for training
kindiana#1016: you have n_layers serial steps when training kindiana#1016: but n_layers * tokens when generating AI_WAIFU#2844: that makes sense. AI_WAIFU#2844: Idea: distill a model using the reverse kl loss, which is mode seeking rather than mode covering. AI_WAIFU#2844: That way it should be have like a gan, making high quality output but dropping most of the distribution AI_WAIFU#2844: So you can get away with a smaller model for generation purposes. Manny96#3437: We should make a DevOps channel - Kanban style? zphang#7252: what do you mean by dual heads zphang#7252: you mean after the next whole transformer block? kindiana#1016: yes zphang#7252: interesting, and when you say late residual add, you mean making this change without re-tuning and hoping that because of residuals it still kind of works? Louis#0144: Span head and AR head kindiana#1016: yup zphang#7252: er elaborate? Louis#0144: Maybe I’m over tired and confusing T5 for something else Louis#0144: lol zphang#7252: or maybe I'm missing something lol zphang#7252: it should just be a standard encoder-decoder? (other than the pos encoding) bmk#1476: uh not sure I see how this cuts down on bandwidth kindiana#1016: this does not
kindiana#1016: but it lets you overlap communication with computation kindiana#1016: so the wallclock is max(comms, compute) kindiana#1016: instead of comms + compute zphang#7252: ben, is it easy for you to share the pretraining scripts for gpt-j? kindiana#1016: wdym? kindiana#1016: should be able to do it from the repo bmk#1476: also what about the backward pass? kindiana#1016: what about it? kindiana#1016: (you can overlap in the backwards pass too) bmk#1476: can you use the same trick backwards? zphang#7252: I'm a little confused about what the entry point is. Would I run `train.py` on the GCE machine and `device_train.py` on each TPU VM? kindiana#1016: train.py handles all that kindiana#1016: although currently the repo is broken because all tpu pods have mismatched python versions lol kindiana#1016: like, node 0 has python 3.8.10 but all the other nodes have 3.8.5 :mesh: zphang#7252: oh so I would only need to run `train.py` on the GCE VM, and that does all the things? neat kindiana#1016: yes kindiana#1016: it creates the tpu, ssh's into them to install the deps, starts the ray workers etc suh#2879: hi everyone, just wondering if it's possible to minibatch or break each step into batches for VQGAN+CLIP, can't get higher resolution on a v100 bc it runs out of memory alstroemeria313#1694: We never did figure out how to do tiles well suh#2879: hmm, it's all good will use topaz atm and look into it later, ty tho
alstroemeria313#1694: If you have two GPUs you can model parallelize VQGAN suh#2879: yeah don't really have access to two gpus atm suh#2879: been using gradient notebooks chilli#5665: By who? :thonk: EricHallahan#1051: https://www.reddit.com/r/EleutherAI/comments/l7uuy4/is_anyone_actually_from_eleutherai_here/ Daj#7482: I don't really know how AMAs happen, but I'm not opposed to doing it if there would be enough interest Sid#2121: I think /r/MachineLearning might be a better venue tho kurumuz#5695: I'm sure there would be interest if you guys decided to do it kurumuz#5695: AMAs are really tiring to do haha kurumuz#5695: you need to answer like, maybe hundreds of questions Daj#7482: I can take a day off to do that, it sounds fun Daj#7482: yeah Dupelet#9080: I'm from r/futurology, and we're on the lookout for topics of interest, which is why I'm asking Dupelet#9080: AMAs (Ask Me Anything) are Reddit's version of Q&A interviews. AMAs on our sub are generally less intense, over a few days. Answer at your convenience, no set time necessary. Daj#7482: lol you had David Kelley on Daj#7482: But yeah, at least for me personally sounds fun, guess I should check with the others StellaAthena#3530: I’m down. StellaAthena#3530: There’s also an official AMA subreddit that tbh we are probably enough of a Big Deal to get a platform on if we wanted pebbles#7130: you may get more interesting questions on some subreddits compared to others Dupelet#9080: Well talk it over, and ping me if you're interested 🙂
Manny96#3437: Use to develop applictions using Python Libtorrent . Pile and Libtorrent Manny96#3437: Libtorrent is really cheap and has data integrity Gurkenglas#7362: What happens if, instead of skip connections, one applies the attention and feedforward layer in parallel? paws#3311: i had no idea that apple had an ai residency program 😮 Sid#2121: that's what gpt-j does Gurkenglas#7362: neat. Gurkenglas#7362: aw man theres like 50% chance this is source attribution error. Gurkenglas#7362: does gpt-j get logit lens? Sid#2121: dunno, don't think anyone ever tried it kindiana#1016: Thought it did? StellaAthena#3530: @nostalgebraist is the person to ask about this. I know it works with GPT-Neo, IDR about GPT-J alstroemeria313#1694: btw https://github.com/zzd1992/Image-Local-Attention is a thing alstroemeria313#1694: i'm trying it rn in ipython, it's actual sliding window attention over 2D feature maps alstroemeria313#1694: CUDA only alstroemeria313#1694: (It has custom ops) alstroemeria313#1694: Also it works w/ double backward() (needed for GAN gradient penalties) alstroemeria313#1694: Not multihead though. alstroemeria313#1694: I wish it were alstroemeria313#1694: Uh, it should let you use self-attention at every resolution of a convnet w/ downsampling or upsampling blocks? alstroemeria313#1694: Instead of only at low resolutions.
alstroemeria313#1694: As in they didn't even provide a CPU impl EricHallahan#1051: That drove me insane in the original StyleGAN. alstroemeria313#1694: CUDA only? EricHallahan#1051: It was. alstroemeria313#1694: oh alstroemeria313#1694: there is a TorchLocalAttention in there too alstroemeria313#1694: which is a pure pytorch slow version alstroemeria313#1694: this runs on cpu and torch.isclose checks out w/ the CUDA version alstroemeria313#1694: i... i would feel better if they had an optimized C++ version alstroemeria313#1694: for cpu EricHallahan#1051: OpenCL/Vulkan/SPIR-V seems to be entirely absent from ML for some reason. kurumuz#5695: tinygrad should be mainly OpenCL kurumuz#5695: https://github.com/geohot/tinygrad EricHallahan#1051: Like there is the Vulkan backend for PyTorch, but that is really just meant for inference on Android. kurumuz#5695: lol kurumuz#5695: maybe CUDA is just faster kurumuz#5695: ¯\_(ツ)_/¯ EricHallahan#1051: You have to compile from source to get it lol Louis#0144: OpenCL is dead Louis#0144: thats why
Louis#0144: lol Louis#0144: besides legacy systems Louis#0144: almost all scientific computing stuff Ive seen is CUDA Louis#0144: with very few exceptions EricHallahan#1051: https://www.khronos.org/blog/opencl-3.0-specification-finalized-and-initial-khronos-open-source-opencl-sdk-released Louis#0144: 🤷‍♂️ alstroemeria313#1694: yeah but what uses it Louis#0144: in 2016-2018 when I was really into super computing Louis#0144: I never saw anyone use open cl Louis#0144: like at all Louis#0144: even now I dont know anyone who usesit Louis#0144: and I still have friends in distributed computing alstroemeria313#1694: oh, blender can use it? alstroemeria313#1694: ok alstroemeria313#1694: ...does it actually work well ersatz#0001: is CUDA a thing on Android? EricHallahan#1051: If you have an NVIDIA GPU, I am sure it is. Louis#0144: Tegra? Louis#0144: is Tegra still a thing? Louis#0144: oh yeah the switch
triggerhappygandi#0001: Is tegra not a thing in android phones anymore? triggerhappygandi#0001: I don't keep up with smartphone tech Louis#0144: smartphone hardware tech is basically solely apple Louis#0144: lol Louis#0144: qualcomm is so incredibly far behind Louis#0144: they arent really worth considering alexyz#3459: for CPUs, yea alexyz#3459: but it depends on what category of hardware you choose to compare EricHallahan#1051: I'm pretty sure that Qualcomm sells multiple times more chips than Apple lol alexyz#3459: but performance wise... kurumuz#5695: CPUs, GPUs and neural engines kurumuz#5695: lol kurumuz#5695: apple is completely smacking everyone else alexyz#3459: when tf do you need a mobile GPU lmao EricHallahan#1051: It helps a lot when accelerating graphics lol alexyz#3459: and "neural engines" are a thing that apple literally made up alexyz#3459: like of course no other smartphone company would have it, it's part of the term EricHallahan#1051: Everyone and their dog is getting on on the trend of dedicated units for the acceleration of ML tasks. alexyz#3459: and by other hardware I meant stuff like screens and other hardware components alexyz#3459: like Samsung rules the smartphone display space, they were supplying Apple exclusively up until a few years ago
ersatz#0001: inference ersatz#0001: Computational photography example kurumuz#5695: i meant any processor that accelerates matrix math. kurumuz#5695: other chips have them too, they're just much slower. tjroxx#2664: hey yall tjroxx#2664: Jamaican here alexyz#3459: 🇯🇲 Siyris#0001: There's Colabs in a lot of the frequently used channels, what about making a Colab dedicated channel under resources where people could share them and pin the most frequently requested ones? someKindaBean#8471: I recently saw a paper about optical matrix math co-processors someKindaBean#8471: That was kind of cool, because it allows operation on a lower time complexity nshepperd#2316: i used to use opencl, but it was a lot of effort to make an entire ml library, so i said screw it and used pytorch instead nshepperd#2316: and we've been living under the boot of the cuda monopoly since... mega b#6696: This has been on my mind: Is humans more of a GAN, or a Transformer? 𓅬 gabriel_syme 𓅬#3220: I'm definitely not a gan thenightocean#6100: humans are more of a GOFAI expert system IMHO 𓅬 gabriel_syme 𓅬#3220: on top of a huge NN? bmk#1476: humans arent really gofai bmk#1476: humans are a NN pretending to be a gofai pretending to be a NN 𓅬 gabriel_syme 𓅬#3220: I think essential to all this is the fact that analytical descriptions of whatever the system is will always fall short of what it is. 𓅬 gabriel_syme 𓅬#3220: that's all I have though lol, no insights on what it is
𓅬 gabriel_syme 𓅬#3220: by that I don't mean model vs reality nonsense, just that the idea of absolute dichotomies not the way to decompose things suh#2879: any ways to stop a pytorch model without memory leaks applepi#4437: in general purpose-built anything is useful for .... the purpose lol mega b#6696: perhaps on a portable vr headset mega b#6696: maybe throw a couple 3090tis, and a a100 why not mega b#6696: now you can train a gpt model and run minecraft shaders + third degree burns mega b#6696: good luck holding that thing up, too applepi#4437: @someKindaBean can you link paper; i'm curious what is meant by "lower time complexity" Kia#2550: Run a Image model:thonk: Louis#0144: Restricted Boltzmann machine natedog#8669: Hi y'all!, my team is trying to finetune GPTNeo (125M) on some additional github data, but the training loss hasn't changed much over our initial test of 200 steps. We're wondering if this is okay to not see much change over such a short period of time or if something might be wrong with our warmup or something. Would appreciate any advice on anyone who's tried finetuning GPTNeo for other purposes Kharr#7888: The model tunes fine. You're going to have to give more information around warmup schedule, learning rate, optimizer, # of tokens/step. What do you mean by 200 steps? 200 optimizer steps or epochs over data or ? E.g. if you're using a linear warmup schedule over 10k steps and you're 200 steps in.. your lr at 200 is lr * 200/10k which means it's barely going to budge EricHallahan#1051: Warmup? Arto#7478: So here some details we are training GPTNeo fro HuggingFace modelhub with raw github data, with all params as per config here https://github.com/EleutherAI/gpt-neo/blob/master/configs/gpt2_small.json except we are using Adafactor, and we did 10k steps with batch size 512 seq_len 2048, which were all warmup from 0 to 2.5e-4 (cause I multiplied number of warmup stepsby grad accumulation twice accidentally 😬 ). The eval loss was fluctuating around 0.92 rather then decreasing. So we wonder if this is expected cause GPTNeo is already quite good with code, of should we search for some bugs? natedog#8669: Even more additional info, by step we refer to number of batches seen and we are using a linear warmup scheduler. If you'd like to see some pretty charts of the behavior we are talking about, here is a link to our wandb run: https://wandb.ai/wandb/hf-flax-gpt-neo-copilot/runs/2yr36dg0?workspace=user- Kharr#7888: Looks normal to me. Your train loss is at ~ 1.5 which means the model already understands the task (assuming you're using the normal tokenizer + pretrained model). It will go down very slowly as you tune and the model refines a bit. There's also a chance that it will get worse since it was already trained on github data and your finetune could break the model. natedog#8669: okay, awesome! Is there anything special we could do to lower the chance that we break it lol? Kharr#7888: Keep lr < 1e-4 and use a big batch size. Noisy training + high LR will wipe out some of its encoding. CRG#8707: Refreshing the optimizer state might be hurting it. (Is there a way to transfer the Adam buffers into adafactor?) Kharr#7888: using slow warmup is fine too
CRG#8707: Yeah, referring to <https://discord.com/channels/729741769192767510/851918317039255592/857653027233333248>, (lower loss but higher over fitting risk) Kharr#7888: For 6B model, yes, but for 125M model overfitting is really hard unless dataset is tiny Arto#7478: Hmm, we can just try to switch to Adam and load the state, will try and see if it'll fit to memory with our current setting so if loading the optimizer state should we decrease number of warmup steps? also we don't mind loosing some performance on general language modeling to make it better with code natedog#8669: Yeah, we are essentially trying to create an open version of GPT Codex and we are using quite a large, but noisy dataset from Github ~209GB compressed, so we mostly care about ability to complete code similar to github copilot. Thank y'all for your help and suggestions. We will try a few different experiments and see which works best bmk#1476: you should try our code model too bmk#1476: https://huggingface.co/lg/ghpy_20k CRG#8707: If trying to replicate everything, it's likely that copilot uses something like the codex-s variant (fine-tuned on function implementations that pass unit tests on a curated problem dataset). <https://github.com/openai/human-eval> Kharr#7888: These are pretty :chonk: models. bmk#1476: it's not chonk if it's less than 100B Kharr#7888: How much data was it trained on? bmk#1476: not a lot, it's a fine tune bmk#1476: I don't remember exactly how much Orz#3023: Is it a model similar to copilot? bmk#1476: I'd assume so Kharr#7888: Looks like 2.7B Neo with all global attention? EricHallahan#1051: It is literally just Neo tuned on Python IIRC. Kharr#7888: Unless config is incorrect, it also did drop the local attention bmk#1476: screw local attention
nostalgebraist#3542: sorry if i'm just out of the loop, but is ghpy-6b going to happen? bmk#1476: it will whenever I get around to it Louis#0144: based and ghpy pilled Orz#3023: is it possible to combine multiple collabs/kernals at the same time to finetune gpt-j? Kharr#7888: No. Finetuning requires a lot more resources than any single Colab offers and training asynchronously would be quite an amazing feat if you can somehow figure that out. sweg#8920: ~~i am a decision tree~~ sweg#8920: actually im a markov chain sweg#8920: if i see the word among us in any sentence i say sus sweg#8920: intelligence is a myth sweg#8920: Unironically though there might be something simpler in humans. Identity and world view are constructed later in life so are learned, and can vary widely between person to person (especially when looking at different time periods), but base drives like survival, reproduction, and pleasure seeking behavior are common among all animals. sweg#8920: I think learned behaviour/stuff is in neocortex in a kind of world model sweg#8920: and that is where a lot of intelligent stuff happens sweg#8920: but all that does is create a representation of the world sweg#8920: idk where motivations come from Louis#0144: Tbh I think most of neuro cog sci is confirmation bias because we as humans believe this is how our brain works Louis#0144: But it is in fact just an artifact of the distributed representation sweg#8920: Agree sweg#8920: yeah its emergent sweg#8920: most things are emergent sweg#8920: but i guess thats kind of stating the obvious
Louis#0144: There was a paper where they asked neuro cog sci scientists to try to understand the cognitive processes of a microchip Louis#0144: And they massively failed Louis#0144: It wasn’t even close Louis#0144: I don’t think it’s obvious at all Louis#0144: lol sweg#8920: well its not really a full answer sweg#8920: "everything is emergent!" sweg#8920: i mean for the neocortex it's kind of understandable sweg#8920: where different sensors wire is determined by genes -> chemical gradients, so neocortex areas for certain sensors is fixed sweg#8920: they work together to create a latent space of representations distractedm1nd#2062: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 This is it btw, it's actually a really great read for anyone who likes to think about "decoding the brain" distractedm1nd#2062: Theres been some pretty good rebuttals though, I'd have to look for them natedog#8669: Yeah we are, if you are interested you can check out our github: https://github.com/ncoop57/gpt-code-clippy and we are planning on evaluating on that human eval benchmark as well as the apps dataset https://github.com/hendrycks/apps kindiana#1016: do you have your dataset somewhere? natedog#8669: No where yet besides the TPUs we got access to. We were actually gonna try and see if we could get them put on "the-eye" similar to how y'all have the pile natedog#8669: because it is similar format as well since we use the same download script chilli#5665: We already evaluate a fine tuned version of gpt-neo in APPS I believe bmk#1476: @-Archivist is the guy to talk to for that
bmk#1476: also I'd be interested in the details of your dataset bmk#1476: how big, what kind of filtering do you do, etc bmk#1476: the biggest problems with our current GitHub set for pile is a) it's only like 600GB and there's a lot more code than that and b) a lot of it is garbage natedog#8669: We use this tool: https://seart-ghs.si.usi.ch/, it only has about ~1million repos max to filter on. We added some additional filtering: - <= 10 stars - <= 2 contributors - < 1 commit - no forks - must have a license - > 70708 bytes repo size This gives us about 500,000 repos and then we merge these with the original repos from the pile (removing dups) which gives around 670,000 repos that we ended up downloading (around 99.6 success rate). We did a bit of testing for duplicate code in a subset of our dataset and found it was quite bad ~28% near duplicates, but we haven't finalized the deduplicate process yet to see how bad it will be for the entire dataset. Yeah I'm guessing they are tons more, I'm just not sure how to get it easily. Maybe a ton of personal tokens to do all of the API calls to github? natedog#8669: Will hit him up, thanks! triggerhappygandi#0001: I think it must've been said already but if you have a large enough data to fine tune on, fine tuning won't be very effective natedog#8669: From the recent OpenAI paper, they found that if you finetune the model will converge faster even though it won't get any improvement on metrics triggerhappygandi#0001: Yeah I was thinking about that itself. triggerhappygandi#0001: But didn't remember the convergence part 😅 -Archivist#7336: Yes, I'll host whatever it is. -Archivist#7336: @bmk Did anyone do anything with the libgen to text output I did for you? > https://the-eye.eu/eleuther_staging/lg_pdf2txt.7z
> https://the-eye.eu/eleuther_staging/lg_epub2txt.7z I joined here thinking @shawwn was some lead and haven't seen him active on anything since 🤷🏼‍♂️ bmk#1476: one problem I noticed is there are a lot of source files that are just a wall of hex constants or something bmk#1476: they're all really big too, so they take up a disproportionate amount of the data bmk#1476: I don't think anyone has used to for anything yet, but if we ever make a pile v2 that's going in there bmk#1476: also the pdf ones would need some cleaning natedog#8669: Dude awesome, thanks!! I'll reach out once we've done a bit more finalizing of it bmk#1476: and by some I mean a lot -Archivist#7336: > pile v2 that's going in there A v2 should happen now I'm here and daft enough to pull in new large sources for you -Archivist#7336: sound -Archivist#7336: true bmk#1476: the main bottleneck on v2 so far has been nobody had time to work on it, but if you wanna take up the lead role on that I'd love to provide help where I can bmk#1476: I can give you a list of things that would probably be worth including that nobody got around to the first time -Archivist#7336: do that asap and Ill get on it, I'm right in thinking it's just lots of coherent plaintext you need right? bmk#1476: yeah basically bmk#1476: and the more the better, the cleaner the better -Archivist#7336: okie dokie -Archivist#7336: I guess that now we're _done_ libgen, I should do scihub too -Archivist#7336: there's still a lot to get out of libgen as I only did the two formats but scihub being lots of technical text and being much larger would be god to get done too
-Archivist#7336: mailing lists? bmk#1476: so here are the individual-set things i can think of off the top of my head: - libgen - scihub - FF.net and AO3 - reddit comment data - the hendrycks AMPS pretraining set (i can get this one) - all the training sets in eval harness (don't worry, ill get this for you, just remind me sometime) - bigger github training set (the github set in pile v1 is pretty smol) - APPS training set (i can get this one) - NaturalQs (i can get this one) -multilingual wikipedia (only english is in v1) bmk#1476: there's also one other big thing: if we can figure out how to extract clean multilingual text from all of commoncrawl, that's an ungodly amount of data and so probably far ourweights everything else on the list in importance bmk#1476: unfortunately that's really nontrivial and i spent a lot of time bashing my head into the wall trying to make it work bmk#1476: so yeah if you decide to tackle any of this i can tell you all about what ive tried so far to hopefully save you some time kindiana#1016: just ask the model nicely :berk: -Archivist#7336: reddit comment data is done, I have original ps data on hand, adding long post body texts would be good too, got those already bmk#1476: the CC one is the one i have most of my hopes pinned on but it would also be a massive massive undertaking bmk#1476: i think multilingual&filtered CC + libgen + scihub + v1 + filtered github could get to 100TB which is a really nice round number
-Archivist#7336: I'll need some dev to bash out some code for that, but will happily run, compress and host it bmk#1476: the problem is that extracting text from arbitrary multilingual websites is really really hard -Archivist#7336: fair bmk#1476: also running that at scale is hard too but i bet you have that down to a t -Archivist#7336: for sure 👌🏼 working with the CC data is fun, had my network hitting upwards of 500Gbit/s before, amazon love it!! AI_WAIFU#2844: Frankly with TPU VMs we're not short of cpu's either, although it's kind of a waste bmk#1476: i mean like the logisitcs, not the resources bmk#1476: but archivist has both kindiana#1016: https://github.com/src-d/datasets/tree/master/PublicGitArchive kindiana#1016: this is kinda sadge bmk#1476: ? kindiana#1016: src-d went out of business lol Sid#2121: there's also https://www.usenetarchives.com/ @-Archivist - I emailed the guy a while back and he seemed open to sharing the whole db -Archivist#7336: HA! It's already on archive.... he just took those and rehosted it bmk#1476: i think figuring out multilingual clean CC is by far the most productive use of time personally Sid#2121: ah ok Sid#2121: well, if it's already available bmk#1476: the amount of data is simply absurd bmk#1476: i downloaded the usenet archive and decided it kinda sucked tbh lol Sid#2121: how so
Sid#2121: it's text -Archivist#7336: Aye, I'll start shoving such things into a pile_v2 staging dir for us Sid#2121: also how do people feel about... twitter bmk#1476: youd need to parse it to get rid of all the header gunk and at that point you dont have muchdata left kindiana#1016: 50 tokens is all you need? :berk: bmk#1476: i do not feel Sid#2121: there's threads tho Sid#2121: i mean, better for images bmk#1476: i wish not to feel about twitter -Archivist#7336: news articles? I've got scrapes from 50+ news sites over the last 7 years Sid#2121: hm, we already have a lot, but more can't hurt as long as we deduplicate well StellaAthena#3530: A bunch of the datasets in the Pile we found by going around USG websites and looking for bulk download buttons StellaAthena#3530: Maybe replicate that with Germany, Russia, Spain, India, China, Japan, ... StellaAthena#3530: Like, there's gotta be at least a dozen versions of the US PTO dataset StellaAthena#3530: This is over a TB of patent applications in a variety of european languages: https://www.epo.org/searching-for-patents/data/bulk-data-sets.html StellaAthena#3530: Israeli legal datasets: https://en-lawlib.tau.ac.il/israeli_databases bmk#1476: i think getting CC is much higher leverage StellaAthena#3530: ~700,000 UN publications: https://digitallibrary.un.org/collection/Documents%20and%20Publications?ln=en StellaAthena#3530: And transcripts of every speech given at the UN: https://digitallibrary.un.org/search?ln=en&cc=Speeches bmk#1476: literally microscopic next to the size of CC
bmk#1476: 700k publications is, what, a few dozen GB at most? StellaAthena#3530: I'm just assuming that filtering multilingual CC is a fools errant tbh nostalgebraist#3542: my 30-day TRC trial is going to expire next week. if possible, i'd love to get it extended. what's the recommended approach for that? i'll fill out the survey they sent me -- just wondering if i should do something beyond that Louis#0144: Email Louis#0144: Talk to them nostalgebraist#3542: thanks! Louis#0144: As long as it isn’t for profit stuff Louis#0144: (duh) Louis#0144: Did u try that @Lucas Nestler (ClashLuke) Louis#0144: lol nostalgebraist#3542: i recently set up a continually running scraper to extend my tumblr dataset, and i really want to be able to finetune again once i've got a nontrivial increase in the dataset size Louis#0144: Continual learning stuff with engrams like what @aero does is pretty promising tbh nostalgebraist#3542: the train loss curve is so beautifully linear in the first epoch... i want to finally have enough to see where it bends Louis#0144: I was considering helping aero with setting up baleen too Louis#0144: Just been busy Teemochu#8740: I noticed when I was dling the pile that you limit to 32 connections from one user at once (not that I needed more, I saturated my gigabit link with about 10, just decided to do it very naively and ran into that error lol) -Archivist#7336: why would one ever need to or mistakenly be running 32+ connections... Teemochu#8740: every file in parallel lol
-Archivist#7336: every... 😄 -Archivist#7336: donut Teemochu#8740: it's called pasting "start wget" commands -Archivist#7336: That dir is only 10Gbit capable, only sees it when we release something new and hn/reddit gets hold of it mind you https://cdn.discordapp.com/attachments/729741769738158194/863901609905750045/AI10g.mp4 Teemochu#8740: ffnet https://archive.org/download/updateablefanfic ao3 https://archive.org/download/AO3_story_dump_continuing Teemochu#8740: just in case you hadn't run across them pacman829#0801: where'd you get those gauges? olives ❀#2305: OH WAIT BELLARD HAS GPT-J 6B MODELS NOW 🔫 THAT'S SO AMAZING 🤩 TYTYTY olives ❀#2305: probably https://the-eye.eu/traffic/ maybe? 𓅬 gabriel_syme 𓅬#3220: the 125M definitely fits in memory with adam, I only had to use adafactor for 1.3b and 2.7b 𓅬 gabriel_syme 𓅬#3220: The EU has translations of most documents in almost all the member languages, could be interesting although I'd imagine that's already been used for translation datasets. olives ❀#2305: Google Translate uses it 👍 \o/#8499: Hi! i dont really know much about AI but i'd like to join the community and contribute open source community \o/#8499: is there any resource where i can start? bmk#1476: https://discord.com/channels/729741769192767510/729741769738158194/801630685525835787 check this out bmk#1476: it has all the resources you need \o/#8499: arigato! \o/#8499: do people here communicate just by chat?
bmk#1476: yeah \o/#8499: nice EricHallahan#1051: All useful communication is textual. \o/#8499: i hope someday ill be of help \o/#8499: : ) \o/#8499: gotta learn at the moment aero#1357: 👀 EricHallahan#1051: What are you 👀ing at. Kia#2550: Probably another ground breaking thing They found in there Bot👀 One#5919: why? EricHallahan#1051: Textual communication is archivable and traceable. kurumuz#5695: attention 👀 EricHallahan#1051: is One#5919: thought processes interacting One#5919: can be done across many different media One#5919: but i concede the point aero#1357: all EricHallahan#1051: you kurumuz#5695: need One#5919: and a supercomputer
mkualquiera#3484: https://cdn.discordapp.com/attachments/729741769738158194/863950934714089482/5g6e9c.jpg pacman829#0801: who is bellard? One#5919: whoa EricHallahan#1051: https://en.wikipedia.org/wiki/Fabrice_Bellard One#5919: https://bellard.org/textsynth/ olives ❀#2305: ^ pacman829#0801: https://tenor.com/view/oh-snap-dave-chappelle-saturday-night-live-oh-no-omg-gif-19226541 One#5919: "The best way to make money from a great idea is to present it to the public. There are many ways in which you can present your product to the public. There is no way to tell how they will react to your product. The best way to invent something original is to follow the guidelines of the world so you don't get confused. All innovators need to be aware of their surroundings and how they can be improved. If you are trying to invent something for the first time then you need to understand the world around you. The best way to" I've been running this prompt on Bellard's site and it gives text that is as good as AI Dungeon's Dragon model. GPT-J is HEAVY yo pacman829#0801: that probably explains why it runs inferences so fast pacman829#0801: thank you
bts-dui#4424: I downloaded pile dataset, and I found that it is only 458GB in total. Is this normal? I expected that It will require about 800GB. StellaAthena#3530: It's compressed kurumuz#5695: god bless markov chains olives ❀#2305: > The best way to achieve this is to study the world and understand how things work. > A man who is looking for gold must always be prepared to follow his mother. > You need to think about the world before you design something or invent anything. olives ❀#2305: https://cdn.discordapp.com/emojis/857359266863775796.png?v=1 One#5919: You changed the template One#5919: "this" instead of novel thing to get advice for olives ❀#2305: https://cdn.discordapp.com/attachments/729741769738158194/863968706677440522/unknown.png olives ❀#2305: i just copied it lol One#5919: ohh, what i'm saying is you need to provide what you want advice for One#5919: my prompt was incomplete, should've had "X" at the end One#5919: "The best way to X ..." olives ❀#2305: ohh olives ❀#2305: > The best way to invent something original is to invent something that somebody already needs. > When someone asks you about your idea, say how the problem that you're addressing is a problem that's unsolved. > Be honest about how hard it is. olives ❀#2305: that makes much more sense EricHallahan#1051: This is not the place to bicker about this.
One#5919: we're discussing proper prompting technique and its effects but i concede the point One#5919: #prompting 𓅬 gabriel_syme 𓅬#3220: woah look at you finishing each other's sentences. true nerd love right there One#5919: domestic bliss someKindaBean#8471: There's a few papers on optical processors that I looked at recently. One on using them for FFTs that talks about time complexity explicitly - https://core.ac.uk/reader/156827770, here's one about using optical processing for convolution that doesn't explicitly discuss complexity - https://openaccess.thecvf.com/content_CVPR_2020/papers/Pad_Efficient_Neural_Vision_Systems_Based_on_Convolutional_Image_Acquisition_CVPR_2020_paper.pdf, here's a company trying to use optical processors for their special sauce - https://neurophos.com/ One#5919: @someKindaBean https://news.ycombinator.com/item?id=27738029 too One#5919: so non-discrete applepi#4437: ah interesting ok it's using optics as a coprocessor to drop the time-work compexity someKindaBean#8471: yep, it's an idea that was first used back in the 70s (or earlier, but I have a paper on optical FFT computation from the early 70s) but got dropped when computing power improved. And now it's making a resurgence, as the link from The One discusses someKindaBean#8471: thanks for that link too - cool stuff applepi#4437: makes sense ;; physical world is confusingly good at doing things haha applepi#4437: did you see the bacteria for TSP https://www.theguardian.com/science/blog/2009/jul/24/bacteria-computer someKindaBean#8471: I haven't seen that one, but I've seen other stuff with using fungi for routing problems pacman829#0801: What's ai dungeon dragon ? One#5919: an RPG site setting that lets you use GPT-3's Da Vinci model (175b parameters) for $10/mo, but it's not "the real thing" chirp#4545: Curious if there’s an ML-powered app that helps you improve the fluency of your spoken language chirp#4545: The app could show you a question, and you would respond out loud, and then the app could rate your response quality & fluency and suggest ways to improve chirp#4545: To me at first glance this app seems (1) really useful, at least to me, and (2) well within the capabilities of current ML tech Parker#3197: doesn't duolingo do that somewhat already? chirp#4545: With spoken language?

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