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This explanation feels unsatisfying. It's so high-level that it's mostly void of any actual information. What was the wrong assumption that the code made that

This explanation feels unsatisfying. It's so high-level that it's mostly void of any actual information. What was the wrong assumption that the code made that caused this wrong behavior? Why was it not caught in the many layers of automated testing before it made its way to production? What process and procedural changes are being implemented to reduce the risk of this class of bug happening again? Presumably all of that is playing out internally, but if the public postmortem is meant to instill confidence, you have to actually share some of the details, or else it becomes meaningless.

nomel 3y ago
> inference kernels produced incorrect results when used in certain GPU configurations. Seems pretty clear. A good interpretation is that they had a test escape for certain GPU configs.
laborcontract 3y ago
The explanation is definitely unsatisfying. If I had to guess it was an issue with quantization. OpenAI moves so quickly with their product and doesn't seem to be shy about quickly pushing changes to prod. There are too many times to count that, using ChatGPT, I've seen the frontend changing before my eyes or seen responses change as backend modifications are pushed. On one hand it's refreshing to see their lack of aversion to push changes. On the other hand, it leads to probably one of the most unpredictable experiences I've had using any consumer product.
- p10_user 3y ago
  Does the API offer "fixed" models that don't change? I was under the impression they did.
- Rastonbury 3y ago
  I find that the most annoying and borderline unethical, hiding changes and nerfs behind the fact the output is probalistic especially when I'm paying the same amount for the product and others are paying for and using it in production. As many have mentioned, GPT-4 ability to code is not the same as 6-8 months ago
  - jorticka 3y ago
    So stop paying then.
  - MVissers 3y ago
    Stop paying for chatgpt and use platform.openai.com instead to pick your model for consistency. I’m still using the original gpt-4-3.14 model.
__MatrixMan__ 3y ago
I think your questions all grew up in a world where the people operating the thing knew some rationalist who could think deductively about its operation. But neural networks... they're an exercise in empiricism. We only ever understood that it works, never why. It's sort of a miracle that it doesn't produce buggy output all the time. What do you tell people when they want to know why the miracles have stopped? Root cause: the gods are angry.
- FridgeSeal 3y ago
  I’d genuinely expect the people who built and operate the thing to have a far better write up than what amounted to “it no worked lol”. Sure, NN’s are opaque, but this org paints itself as the herald and shepard of ai and they just produced a write up that’s hardly worthy of a primary-school-child’s account of their recent holiday.
  - thethimble 3y ago
    Random sampling issues due to problems with the inference kernels on certain GPU configurations. This seems like a clear root cause and has nothing to do with the magic of NNs. I don’t understand what the fuss is about.
    - throwuwu 3y ago
      They just want to reinforce their own bias that OpenAI BAD and DUMB, rationalism GOOD! When it’s their own fault for not understanding enough theory to know what happens if there were to be a loss of precision in the predicted embedding vector that maps to a token. If enough decimal places are lopped off or nudged then that moves the predicted vector slightly away from where it should be and you get a nearby token instead. Instant aphasia. The report said it was a GPU configuration problem so my guess is the wrong precision was used in some number of GPUs but I have no idea how they configure their cluster so take that with a giant grain of salt.
      - cqqxo4zV46cp 3y ago
chime 3y ago
I hope I'm allowed to share GPT output here because I asked GPT4 to explain OpenAI's postmortem message and this is what it said: > Large language models (LLMs) like GPT operate using statistics to predict the next word in a sentence. Think of it like a highly sophisticated version of your phone's autocorrect that predicts what you're going to type next, but on a much larger scale. > The system represents words as numbers, or "tokens," as part of its internal language. It uses these tokens to generate text by selecting them based on how likely they are to come after what's already been written. > On February 20, 2024, an update meant to improve the system introduced a glitch in the part of the model that picks out these numbers. Imagine trying to grab a red marble from a bag without looking and accidentally grabbing a blue one instead—similarly, the model grabbed the wrong tokens. > Because of this, the system started spewing out words that didn't make sense together. It's like if your autocorrect suddenly started inserting random, incorrect words into your sentences. > The technical side of the bug involved something called "inference kernels," which are part of the system's operations. When used with certain types of graphics processing units (GPUs)—special hardware to process data—the kernels didn't work properly. > Once the error was spotted, a correction was made to the system. After the fix, everything went back to normal, and the model resumed generating coherent responses. It would be better if they elaborated on what "certain GPU configurations" meant because that's basically the central piece here.
- refulgentis 3y ago
  People do it, and it's not against the rules: but, it has died down asymptotically. Though I did see a couple top comments that hid it surviving for ~4 hours this week. It usually gets downvoted for banal aspects, but ones I've come to agree are important: - oftentimes it's a lightly edited copy-and-paste of an attempt to summarize an article. - even with edits, they're extremely long (this is edited, and its 250 words, about 1 page and 1/2 my browser viewport at 4K) - usually off-topic because it's too broadly on-topic, i.e. its a summary of the article - ex. here, it isn't germane to the comment it's replying to other than 'if you want more info from them, ask what GPUs' -- it's unlikely the commenter needed the whole article ELI5'd to them in reply to their observation they'd like more info Sort of "grey goo" for conversation, even with best intentions and editing applied.
- 3y ago
hn_throwaway_99 3y ago
I had the exact opposite reaction. I am in no way an AI expert (or novice for that matter), but I generally have an understanding of how tokenization works and how LLMs parse text strings into a series of tokens. Thus, I thought this paragraph was particularly well-written in a manner that explained pretty clearly what happened, but in a manner accessible to a layperson like me: > In this case, the bug was in the step where the model chooses these numbers. Akin to being lost in translation, the model chose slightly wrong numbers, which produced word sequences that made no sense. I liked this because when I first saw the example word salads I was so impressed by them - they look to be syntactically correct, but semantically they're gibberish. But knowing the basics of how LLMs choose the next token let me imagine some bugs where the "lookup table" if you will of word-to-token or vice versa (and I realize that may not be exactly the best analogy) was slightly offset. Further, this sentence, "More technically, inference kernels produced incorrect results when used in certain GPU configurations." led me to understand how this could make it out into production - I'm sure ChatGPT has to be using tons of different GPUs in different configs to handle their volume, so it's understandable to me that their was a new matrix of config settings + code that made this bug show up. I don't say any of the above to "excuse" OpenAI, but I also don't think they need any "excusing" to begin with. I don't think this was an unforgivable bug, and I appreciate them being so quick to explain what happened.
- ummonk 3y ago
  No, that just explains the symptom of the bug, not the underlying bug, how it came about, and how they can prevent it from happening again. "More technically, inference kernels produced incorrect results when used in certain GPU configurations" has zero technical detail. The only information it is providing us is that the bug only showed up in some GPU configurations.
  - PeterisP 3y ago
    I feel that it does have relevant technical detail - as far as I understand, any inference kernel is "supposed to" give the same outputs anyways, so if it produces incorrect results in certain GPU configurations, that indicates some flaw in the GPU/microcode/driver in some new hardware they introduced.
iforgotpassword 3y ago
To be honest > On February 20, 2024, an optimization to the user experience At that point, about 10 words in, I already wanted to stop reading because it starts with the "we only wanted the best for our customers" bullshit newspeak. Anyone else going off on that stuff too? I'm pretty much already conditioned to expect whatever company is messaging me that way to take away some feature, increase pricing, or otherwise piss me off. In that case it was "not give any interesting detail at all".
- renonce 3y ago
  That sounds like a good use case for GPT. A GPT that automatically highlights such corporate speak and hints “WARNING: bullshit ahead”. I’m 100% sure it’s technically very easy to engineer such a model. Do you think OpenAI’s superalignment will ever allow you to make such a model?
  - passwordoops 3y ago
    I think Q* was going to provide exactly this. And a unicorn that poops ice cream for everyone
  - nottorp 3y ago
    ... but on the contrary, all the public services are programmed to bullshit you. Every time I ask a question, the answer could be 3x smaller. Even if it's right and not a hallucination.
  - jstarfish 3y ago
    You don't need a whole fucking model for this, you just need to invoke the right arcana. Try: > Act as a professional skeptic. Assess the passage for any deceptive, manipulative, or dishonest elements, including instances of doublespeak, inconsistency, fraud, disingenuity, deception, or sophistry: It works on ChatGPT 3.5 as long as you're not trying to ask it questions about the Talmud. That will quickly devolve into handwavy "well, it's historically complex and difficult to understand" bullshit.
    - 3y ago
bjackman 3y ago
I don't a the problem with this communication, except they just shouldn't call it a "postmortem". In a medical setting this is equivalent to saying "the patient was coughing up blood, and now they're dead". It's relaying some potentially useful info, and I guess it's _technically_ a post-mortem analysis but it doesn't fit expectations for the phrase!
- tomas789 3y ago
  It is an industry standard word by now.
  - mbel 3y ago
    Standard word which implies some level of analysis into causes of the incident. This brief note doesn’t live up to industry practice either.
  - prmoustache 3y ago
    The standard imply analysis and measures taken for similar issues to not happen.
- gtirloni 3y ago
  https://sre.google/sre-book/postmortem-culture/ https://sre.google/sre-book/postmortem-culture/
  - bjackman 3y ago
    Yeah that's what I'm referring to. This isn't a postmortem as described in that book! This is just "it was broken, and then we fixed it". Which is fine, it's good to acknowledge that. Just don't call it a PM!
cfn 3y ago
I don't see the issue, in this type of software a one off bug in tokenization, for example, would create the type of nonsense people saw. That would make sense given their high level explanation.
deleted 3y ago
[deleted]
rmbyrro 3y ago
"in certain GPU configurarions" gives a hint for why it wasn't caught in automated testing. It looks like it was a mandelbug, which is hard to catch in a test environment.
mavamaarten 3y ago
Honestly, I don't think we're entitled to a deep technical write-up. We are users of their product, they messed something up, and that's it. Unless they signed an SLA agreement, it's just shit that happens. They don't have to give a technical view into their internal kitchen if they don't want to.
- pfsalter 3y ago
  The biggest problem is that if you're using this in production then it could cause problems. It's not like a traditional outage where you get a load of 503 errors, the system appears to be working correctly but generates gibberish. If you're using this in a chat bot you could be liable for things it's saying...
Zetobal 3y ago
FP is not deterministic across GPU generations. They basically say with a lot of mambo jambo that they deployed code on the wrong GPU architecture.
jasonjmcghee 3y ago
Huh. Thought it was pretty clear. I read it as: ~ "We tried a new optimization technique that modifies how next token candidates are chosen and there was a bug." That would definitely produce the behavior people saw and makes sense.

nomel 3y ago

> inference kernels produced incorrect results when used in certain GPU configurations. Seems pretty clear. A good interpretation is that they had a test escape for certain GPU configs.

laborcontract 3y ago

The explanation is definitely unsatisfying. If I had to guess it was an issue with quantization. OpenAI moves so quickly with their product and doesn't seem to be shy about quickly pushing changes to prod. There are too many times to count that, using ChatGPT, I've seen the frontend changing before my eyes or seen responses change as backend modifications are pushed. On one hand it's refreshing to see their lack of aversion to push changes. On the other hand, it leads to probably one of the most unpredictable experiences I've had using any consumer product.

p10_user 3y ago
Does the API offer "fixed" models that don't change? I was under the impression they did.
Rastonbury 3y ago
I find that the most annoying and borderline unethical, hiding changes and nerfs behind the fact the output is probalistic especially when I'm paying the same amount for the product and others are paying for and using it in production. As many have mentioned, GPT-4 ability to code is not the same as 6-8 months ago
- jorticka 3y ago
  So stop paying then.
- MVissers 3y ago
  Stop paying for chatgpt and use platform.openai.com instead to pick your model for consistency. I’m still using the original gpt-4-3.14 model.

__MatrixMan__ 3y ago

I think your questions all grew up in a world where the people operating the thing knew some rationalist who could think deductively about its operation. But neural networks... they're an exercise in empiricism. We only ever understood that it works, never why. It's sort of a miracle that it doesn't produce buggy output all the time. What do you tell people when they want to know why the miracles have stopped? Root cause: the gods are angry.

FridgeSeal 3y ago
I’d genuinely expect the people who built and operate the thing to have a far better write up than what amounted to “it no worked lol”. Sure, NN’s are opaque, but this org paints itself as the herald and shepard of ai and they just produced a write up that’s hardly worthy of a primary-school-child’s account of their recent holiday.
- thethimble 3y ago
  Random sampling issues due to problems with the inference kernels on certain GPU configurations. This seems like a clear root cause and has nothing to do with the magic of NNs. I don’t understand what the fuss is about.
  - throwuwu 3y ago
    They just want to reinforce their own bias that OpenAI BAD and DUMB, rationalism GOOD! When it’s their own fault for not understanding enough theory to know what happens if there were to be a loss of precision in the predicted embedding vector that maps to a token. If enough decimal places are lopped off or nudged then that moves the predicted vector slightly away from where it should be and you get a nearby token instead. Instant aphasia. The report said it was a GPU configuration problem so my guess is the wrong precision was used in some number of GPUs but I have no idea how they configure their cluster so take that with a giant grain of salt.
    - cqqxo4zV46cp 3y ago

chime 3y ago

I hope I'm allowed to share GPT output here because I asked GPT4 to explain OpenAI's postmortem message and this is what it said: > Large language models (LLMs) like GPT operate using statistics to predict the next word in a sentence. Think of it like a highly sophisticated version of your phone's autocorrect that predicts what you're going to type next, but on a much larger scale. > The system represents words as numbers, or "tokens," as part of its internal language. It uses these tokens to generate text by selecting them based on how likely they are to come after what's already been written. > On February 20, 2024, an update meant to improve the system introduced a glitch in the part of the model that picks out these numbers. Imagine trying to grab a red marble from a bag without looking and accidentally grabbing a blue one instead—similarly, the model grabbed the wrong tokens. > Because of this, the system started spewing out words that didn't make sense together. It's like if your autocorrect suddenly started inserting random, incorrect words into your sentences. > The technical side of the bug involved something called "inference kernels," which are part of the system's operations. When used with certain types of graphics processing units (GPUs)—special hardware to process data—the kernels didn't work properly. > Once the error was spotted, a correction was made to the system. After the fix, everything went back to normal, and the model resumed generating coherent responses. It would be better if they elaborated on what "certain GPU configurations" meant because that's basically the central piece here.

refulgentis 3y ago
People do it, and it's not against the rules: but, it has died down asymptotically. Though I did see a couple top comments that hid it surviving for ~4 hours this week. It usually gets downvoted for banal aspects, but ones I've come to agree are important: - oftentimes it's a lightly edited copy-and-paste of an attempt to summarize an article. - even with edits, they're extremely long (this is edited, and its 250 words, about 1 page and 1/2 my browser viewport at 4K) - usually off-topic because it's too broadly on-topic, i.e. its a summary of the article - ex. here, it isn't germane to the comment it's replying to other than 'if you want more info from them, ask what GPUs' -- it's unlikely the commenter needed the whole article ELI5'd to them in reply to their observation they'd like more info Sort of "grey goo" for conversation, even with best intentions and editing applied.
3y ago

hn_throwaway_99 3y ago

I had the exact opposite reaction. I am in no way an AI expert (or novice for that matter), but I generally have an understanding of how tokenization works and how LLMs parse text strings into a series of tokens. Thus, I thought this paragraph was particularly well-written in a manner that explained pretty clearly what happened, but in a manner accessible to a layperson like me: > In this case, the bug was in the step where the model chooses these numbers. Akin to being lost in translation, the model chose slightly wrong numbers, which produced word sequences that made no sense. I liked this because when I first saw the example word salads I was so impressed by them - they look to be syntactically correct, but semantically they're gibberish. But knowing the basics of how LLMs choose the next token let me imagine some bugs where the "lookup table" if you will of word-to-token or vice versa (and I realize that may not be exactly the best analogy) was slightly offset. Further, this sentence, "More technically, inference kernels produced incorrect results when used in certain GPU configurations." led me to understand how this could make it out into production - I'm sure ChatGPT has to be using tons of different GPUs in different configs to handle their volume, so it's understandable to me that their was a new matrix of config settings + code that made this bug show up. I don't say any of the above to "excuse" OpenAI, but I also don't think they need any "excusing" to begin with. I don't think this was an unforgivable bug, and I appreciate them being so quick to explain what happened.

ummonk 3y ago
No, that just explains the symptom of the bug, not the underlying bug, how it came about, and how they can prevent it from happening again. "More technically, inference kernels produced incorrect results when used in certain GPU configurations" has zero technical detail. The only information it is providing us is that the bug only showed up in some GPU configurations.
- PeterisP 3y ago
  I feel that it does have relevant technical detail - as far as I understand, any inference kernel is "supposed to" give the same outputs anyways, so if it produces incorrect results in certain GPU configurations, that indicates some flaw in the GPU/microcode/driver in some new hardware they introduced.

iforgotpassword 3y ago

To be honest > On February 20, 2024, an optimization to the user experience At that point, about 10 words in, I already wanted to stop reading because it starts with the "we only wanted the best for our customers" bullshit newspeak. Anyone else going off on that stuff too? I'm pretty much already conditioned to expect whatever company is messaging me that way to take away some feature, increase pricing, or otherwise piss me off. In that case it was "not give any interesting detail at all".

renonce 3y ago
That sounds like a good use case for GPT. A GPT that automatically highlights such corporate speak and hints “WARNING: bullshit ahead”. I’m 100% sure it’s technically very easy to engineer such a model. Do you think OpenAI’s superalignment will ever allow you to make such a model?
- passwordoops 3y ago
  I think Q* was going to provide exactly this. And a unicorn that poops ice cream for everyone
- nottorp 3y ago
  ... but on the contrary, all the public services are programmed to bullshit you. Every time I ask a question, the answer could be 3x smaller. Even if it's right and not a hallucination.
- jstarfish 3y ago
  You don't need a whole fucking model for this, you just need to invoke the right arcana. Try: > Act as a professional skeptic. Assess the passage for any deceptive, manipulative, or dishonest elements, including instances of doublespeak, inconsistency, fraud, disingenuity, deception, or sophistry: It works on ChatGPT 3.5 as long as you're not trying to ask it questions about the Talmud. That will quickly devolve into handwavy "well, it's historically complex and difficult to understand" bullshit.
  - 3y ago

bjackman 3y ago

I don't a the problem with this communication, except they just shouldn't call it a "postmortem". In a medical setting this is equivalent to saying "the patient was coughing up blood, and now they're dead". It's relaying some potentially useful info, and I guess it's _technically_ a post-mortem analysis but it doesn't fit expectations for the phrase!

tomas789 3y ago
It is an industry standard word by now.
- mbel 3y ago
  Standard word which implies some level of analysis into causes of the incident. This brief note doesn’t live up to industry practice either.
- prmoustache 3y ago
  The standard imply analysis and measures taken for similar issues to not happen.
gtirloni 3y ago
https://sre.google/sre-book/postmortem-culture/ https://sre.google/sre-book/postmortem-culture/
- bjackman 3y ago
  Yeah that's what I'm referring to. This isn't a postmortem as described in that book! This is just "it was broken, and then we fixed it". Which is fine, it's good to acknowledge that. Just don't call it a PM!

cfn 3y ago

I don't see the issue, in this type of software a one off bug in tokenization, for example, would create the type of nonsense people saw. That would make sense given their high level explanation.

deleted 3y ago

[deleted]

rmbyrro 3y ago

"in certain GPU configurarions" gives a hint for why it wasn't caught in automated testing. It looks like it was a mandelbug, which is hard to catch in a test environment.

mavamaarten 3y ago

Honestly, I don't think we're entitled to a deep technical write-up. We are users of their product, they messed something up, and that's it. Unless they signed an SLA agreement, it's just shit that happens. They don't have to give a technical view into their internal kitchen if they don't want to.

pfsalter 3y ago
The biggest problem is that if you're using this in production then it could cause problems. It's not like a traditional outage where you get a load of 503 errors, the system appears to be working correctly but generates gibberish. If you're using this in a chat bot you could be liable for things it's saying...

Zetobal 3y ago

FP is not deterministic across GPU generations. They basically say with a lot of mambo jambo that they deployed code on the wrong GPU architecture.

jasonjmcghee 3y ago

Huh. Thought it was pretty clear. I read it as: ~ "We tried a new optimization technique that modifies how next token candidates are chosen and there was a bug." That would definitely produce the behavior people saw and makes sense.