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Read the article you linked. It literally says that the GPU is deterministic, the NVIDIA libraries on top are deterministic, but it is Tensorflow that introduc

Read the article you linked. It literally says that the GPU is deterministic, the NVIDIA libraries on top are deterministic, but it is Tensorflow that introduces variability (errors!) for “performance”. My argument is that it is the AI/ML code that is introducing non-determinism, usually by sacrificing repeatability to gain performance. That's precisely what's happening here. Tensorflow introduced a "harmless"[1] data race to improve performance by not having to use a deterministic but slower algorithm. The individual floating point computations are deterministic, it's the multi-threaded design on top that's introducing the variability in the output. [1] Used to be harmless, but cutting corners like this will make it nigh impossible to repeatably validate the safety of future models like GPT5. That seems pretty dangerous...

n2d4 3y ago
As the article says, cuBLAS is deterministic, but other CUDA primitives (eg. some of those in cudNN) are not. Yes, the non-determinism is being introduced somewhere, but that is splitting hairs. The point is that the primitives that you work with on GPUs are non-deterministic by design. I mostly take issue with you phrasing it as a bug and using it to insult the authors.
- xyzzy_plugh 3y ago
  How is that splitting hairs? > The point is that the primitives that you work with on GPUs are non-deterministic by design. This is just blatantly wrong. There are _some_ operations that can be non-deterministic in some scenarios but they are not necessary. GPUs are deterministic. If you ask them to add a million floats in order, you get the same result every time. If you ask them to add a million floats in some arbitrary order, then you may get different results every time. The distinction is that someone had to ask the GPU to do that. It's a choice. > I mostly take issue with you phrasing it as a bug and using it to insult the authors. It's a bug, whether it insults the authors or not is irrelevant. It's most definitely a bug.
  - spott 3y ago
    Basically any parallel map-reduce operation using non-commutative reduce operators[0] is non-deterministic unless you specifically sort after/during the gather, or block on the gather (and gather to a thread-determined memory location). Sorting and blocking takes time. If you remove the sort/block, you will get a non-deterministic answer when operating on floats for a wide variety of reduce operations, but it will be faster. This is true of any parallel map-reduce, done anywhere (MPI, cuda kernels, openMP, spark, etc.), and is not unique to gpus/cuda. > If you ask them to add a million floats in order, you get the same result every time. There are a bunch of ways to add a million floats in order on a gpu, but they will all get you different results.: * split the million floats into ‘n’ chunks, each chunk is summed, then you sum the ‘n’ results. * if you sum results as they are gathered (you don’t need to block) you will get a non-deterministic result, as the threads finishing (outside of a warp) is non-deterministic in order. * if you change ‘n’, your result will change. * if you sort after gathering , your result will change. TLDR: parallel race-conditions are nondeterministic. Map-reduce has an underlying race-condition that you can prevent, but it costs time/performance. Sometimes you don’t care about the non-determinism enough to pay the performance penalty to fix it. [0] https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/icsecomp14seip-seipid15-p.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/...
    - dlkf 3y ago
      Your comment, along with cpgxiii and n2d4’s are all really good. I have a question: suppose training and inference of an LLM were made to be deterministic at the cost of performance. Would the cost be “everything will take twice as long” or would it be more like “inference will take a week and training will take a couple lifetimes”? If it’s the latter, then it seems disingenuous to call this a “bug.” It’s like saying F1 cars could be horse drawn, and they only use internal combustion for “performance reasons.” If its the former, then maybe there is a more interesting discussion to be had about the potential benefits of determinism? (That said, I agree with n2d4 that it’s stupid to insult the authors. Talk is cheap and building is hard.)
      - xyzzy_plugh 3y ago
deleted 3y ago
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n2d4 3y ago

As the article says, cuBLAS is deterministic, but other CUDA primitives (eg. some of those in cudNN) are not. Yes, the non-determinism is being introduced somewhere, but that is splitting hairs. The point is that the primitives that you work with on GPUs are non-deterministic by design. I mostly take issue with you phrasing it as a bug and using it to insult the authors.

xyzzy_plugh 3y ago
How is that splitting hairs? > The point is that the primitives that you work with on GPUs are non-deterministic by design. This is just blatantly wrong. There are _some_ operations that can be non-deterministic in some scenarios but they are not necessary. GPUs are deterministic. If you ask them to add a million floats in order, you get the same result every time. If you ask them to add a million floats in some arbitrary order, then you may get different results every time. The distinction is that someone had to ask the GPU to do that. It's a choice. > I mostly take issue with you phrasing it as a bug and using it to insult the authors. It's a bug, whether it insults the authors or not is irrelevant. It's most definitely a bug.
- spott 3y ago
  Basically any parallel map-reduce operation using non-commutative reduce operators[0] is non-deterministic unless you specifically sort after/during the gather, or block on the gather (and gather to a thread-determined memory location). Sorting and blocking takes time. If you remove the sort/block, you will get a non-deterministic answer when operating on floats for a wide variety of reduce operations, but it will be faster. This is true of any parallel map-reduce, done anywhere (MPI, cuda kernels, openMP, spark, etc.), and is not unique to gpus/cuda. > If you ask them to add a million floats in order, you get the same result every time. There are a bunch of ways to add a million floats in order on a gpu, but they will all get you different results.: * split the million floats into ‘n’ chunks, each chunk is summed, then you sum the ‘n’ results. * if you sum results as they are gathered (you don’t need to block) you will get a non-deterministic result, as the threads finishing (outside of a warp) is non-deterministic in order. * if you change ‘n’, your result will change. * if you sort after gathering , your result will change. TLDR: parallel race-conditions are nondeterministic. Map-reduce has an underlying race-condition that you can prevent, but it costs time/performance. Sometimes you don’t care about the non-determinism enough to pay the performance penalty to fix it. [0] https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/icsecomp14seip-seipid15-p.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/...
  - dlkf 3y ago
    Your comment, along with cpgxiii and n2d4’s are all really good. I have a question: suppose training and inference of an LLM were made to be deterministic at the cost of performance. Would the cost be “everything will take twice as long” or would it be more like “inference will take a week and training will take a couple lifetimes”? If it’s the latter, then it seems disingenuous to call this a “bug.” It’s like saying F1 cars could be horse drawn, and they only use internal combustion for “performance reasons.” If its the former, then maybe there is a more interesting discussion to be had about the potential benefits of determinism? (That said, I agree with n2d4 that it’s stupid to insult the authors. Talk is cheap and building is hard.)
    - xyzzy_plugh 3y ago

deleted 3y ago

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