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Why is TPU support important to openAI? They run their code on Microsoft servers. Only a tiny percentage of people use colab for deep learning. Also, if you s

Why is TPU support important to openAI? They run their code on Microsoft servers. Only a tiny percentage of people use colab for deep learning. Also, if you search PyTorch tpu you can find details of preliminary support from Google. PyTorch will make their engineers and scientists a decent amount more productive. I don't see how that's unintelligent at all.

sillysaurusx 7y ago
Because TPUs are the only way to fit 300GB backprop onto a single device. You literally can't train models on GPUs when they require 300GB for backprop. Not unless you do model parallelization, which isn't always possible (and is significantly more engineering effort than "just run the model"). When you have policies like this, you lose out on such advantages. Especially for infrastructure purposes.
- shoyer 7y ago
  I think TPUs are great, but I don't understand what you mean by saying they can support "300 GB backprop" on a single device. A TPU v3 has 16 GB of high-bandwidth memory per TPU core: https://cloud.google.com/tpu/docs/system-architecture https://cloud.google.com/tpu/docs/system-architecture Sure, you can network together a bunch of TPUs to get access to more memory (in either a data parallel or model parallel way), but that doesn't give you more memory on the same chip. It's basically the same way you would do things on a GPU cluster.
  - choppaface 7y ago
    A single TPU v3 has 8 cores, so that’s 128GB memory total, which is more than any single GPU currently. The TPU software does data parallelism (in Tensorflow) transparently, and it’s somewhat easier to do model parallelism because the memory link is solid and requires no special setup / drivers. You’ll still get an OOM from XLA if you have a tensor that won’t fit in the 16GB of a single core. TPU pods are easier to use than clusters of infiniband-linked volta boxes. For TPUs you just give GCE money and make some small changes to your use of the TPU API. For the volta cluster you’d probably need to bring your own orchestration (e.g. Horovod). So a TPU pod is easier for one person to use and admin currently.
  - sillysaurusx 7y ago
    Proof that a TPUv2-8 can do 300GB of backprop: https://twitter.com/theshawwn/status/1196183733755355138 https://twitter.com/theshawwn/status/1196183733755355138 Think of a TPU as a box with a CPU, RAM, and eight GPUs. In the same way that you can run code on either the GPUs or the CPU, you can run code on the TPU's CPU. When you run code on the TPU's CPU, you have access to up to 300GB before OOMing. It's distinct from running on the TPU cores, which gives you only 8GB for TPUv2 and 16GB for TPUv3, as you say. I use this technique regularly. All you have to do is tf.device(None): # ops go here The TPU's CPU is pretty fast. Normally it's only used for input pipeline transformations. I have no idea why. We use it for actual backprop on massive models. (I call this "coreless mode" because "TPU's CPU" is a confusing mouthful.) For example, right now we're training GPT-2 117M with a 25k context window on 47 TPUv3-8's: https://tensorboard.dev/experiment/idXs4PGOTEe1Jl6g3tq4qA/ https://tensorboard.dev/experiment/idXs4PGOTEe1Jl6g3tq4qA/ 25k context window is far, far out of reach of any GPU for GPT-2. You can verify this is true by fine-tuning GPT-2 1.5B on Colab using a TPUv2-8: https://colab.research.google.com/drive/1BXry0kcm869-RVHHiY6NZmY9uBzbkf1Q https://colab.research.google.com/drive/1BXry0kcm869-RVHHiY6... If a TPUv2-8 only had access to 8GB, it would be impossible to train GPT-2 1.5B, let alone using Adam with a batch size > 1. EDIT: Here's a simpler notebook: https://colab.research.google.com/drive/1ohuxvB7nuvcjpLLIF1L3WR7SSzFENwQY https://colab.research.google.com/drive/1ohuxvB7nuvcjpLLIF1L... !git clone https://github.com/shawwn/gpt-2 /content/gpt-2 %cd gpt-2 !pip3 install -r requirements.txt !python3 download_model.py 1558M !python3 train.py --dataset train.py --model_name 1558M --optimizer adam --batch_size 4 GPT-2 1.5B with Adam + batch size 4 works great on a TPUv2-8. https://i.imgur.com/w8T5CQI.png https://i.imgur.com/w8T5CQI.png
    - p1esk 7y ago
      I don't get your excitement. How is this different from using 8xGPU box? If you use eight Quadro 8000 cards you have access to 384GB of memory to train your models.
      - sillysaurusx 7y ago
        Mostly because TPUs are in reach of hobbyists. After all, it runs on Colab for free. In a business context, TPUs seem far cheaper. A preemptible TPUv2-8 only costs $1.35/hr. It looks like 8x Quadro 8000's would cost >$40k.

sillysaurusx 7y ago

Because TPUs are the only way to fit 300GB backprop onto a single device. You literally can't train models on GPUs when they require 300GB for backprop. Not unless you do model parallelization, which isn't always possible (and is significantly more engineering effort than "just run the model"). When you have policies like this, you lose out on such advantages. Especially for infrastructure purposes.

shoyer 7y ago
I think TPUs are great, but I don't understand what you mean by saying they can support "300 GB backprop" on a single device. A TPU v3 has 16 GB of high-bandwidth memory per TPU core: https://cloud.google.com/tpu/docs/system-architecture https://cloud.google.com/tpu/docs/system-architecture Sure, you can network together a bunch of TPUs to get access to more memory (in either a data parallel or model parallel way), but that doesn't give you more memory on the same chip. It's basically the same way you would do things on a GPU cluster.
- choppaface 7y ago
  A single TPU v3 has 8 cores, so that’s 128GB memory total, which is more than any single GPU currently. The TPU software does data parallelism (in Tensorflow) transparently, and it’s somewhat easier to do model parallelism because the memory link is solid and requires no special setup / drivers. You’ll still get an OOM from XLA if you have a tensor that won’t fit in the 16GB of a single core. TPU pods are easier to use than clusters of infiniband-linked volta boxes. For TPUs you just give GCE money and make some small changes to your use of the TPU API. For the volta cluster you’d probably need to bring your own orchestration (e.g. Horovod). So a TPU pod is easier for one person to use and admin currently.
- sillysaurusx 7y ago
  Proof that a TPUv2-8 can do 300GB of backprop: https://twitter.com/theshawwn/status/1196183733755355138 https://twitter.com/theshawwn/status/1196183733755355138 Think of a TPU as a box with a CPU, RAM, and eight GPUs. In the same way that you can run code on either the GPUs or the CPU, you can run code on the TPU's CPU. When you run code on the TPU's CPU, you have access to up to 300GB before OOMing. It's distinct from running on the TPU cores, which gives you only 8GB for TPUv2 and 16GB for TPUv3, as you say. I use this technique regularly. All you have to do is tf.device(None): # ops go here The TPU's CPU is pretty fast. Normally it's only used for input pipeline transformations. I have no idea why. We use it for actual backprop on massive models. (I call this "coreless mode" because "TPU's CPU" is a confusing mouthful.) For example, right now we're training GPT-2 117M with a 25k context window on 47 TPUv3-8's: https://tensorboard.dev/experiment/idXs4PGOTEe1Jl6g3tq4qA/ https://tensorboard.dev/experiment/idXs4PGOTEe1Jl6g3tq4qA/ 25k context window is far, far out of reach of any GPU for GPT-2. You can verify this is true by fine-tuning GPT-2 1.5B on Colab using a TPUv2-8: https://colab.research.google.com/drive/1BXry0kcm869-RVHHiY6NZmY9uBzbkf1Q https://colab.research.google.com/drive/1BXry0kcm869-RVHHiY6... If a TPUv2-8 only had access to 8GB, it would be impossible to train GPT-2 1.5B, let alone using Adam with a batch size > 1. EDIT: Here's a simpler notebook: https://colab.research.google.com/drive/1ohuxvB7nuvcjpLLIF1L3WR7SSzFENwQY https://colab.research.google.com/drive/1ohuxvB7nuvcjpLLIF1L... !git clone https://github.com/shawwn/gpt-2 /content/gpt-2 %cd gpt-2 !pip3 install -r requirements.txt !python3 download_model.py 1558M !python3 train.py --dataset train.py --model_name 1558M --optimizer adam --batch_size 4 GPT-2 1.5B with Adam + batch size 4 works great on a TPUv2-8. https://i.imgur.com/w8T5CQI.png https://i.imgur.com/w8T5CQI.png
  - p1esk 7y ago
    I don't get your excitement. How is this different from using 8xGPU box? If you use eight Quadro 8000 cards you have access to 384GB of memory to train your models.
    - sillysaurusx 7y ago
      Mostly because TPUs are in reach of hobbyists. After all, it runs on Colab for free. In a business context, TPUs seem far cheaper. A preemptible TPUv2-8 only costs $1.35/hr. It looks like 8x Quadro 8000's would cost >$40k.