tensorflow VS Pytorch

Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

You can use Tensorflow's deep learning API for this.

This is indeed the bread-and-butter, but there is use of all sorts of standard linear algebra algorithms. You can check various xla-related (accelerated linear algebra) folders in tensorflow or torch folders in pytorch to see the list of what is used [1],[2]

[1] https://github.com/tensorflow/tensorflow/tree/8d9b35f442045b...

[2] https://github.com/pytorch/pytorch/blob/6e3e3dd477e0fb9768ee...

2. TensorFlow Developed by the Google Brain team, TensorFlow is a powerful open-source machine learning framework that’s perfect for deep learning and neural network projects. With TensorFlow, you can build and train complex models using an intuitive and flexible API, making it an essential tool for any data scientist looking to delve into deep learning.

I found the official Tensorflow V1 code from a Github branch here (https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/models/rnn/ptb/ptb_word_lm.py). All code necessary to run that file is in the /ptb folder (except data).

In their report they show the 10 projects with the biggest number of contributors. The first one is microsoft/vscode with 19.8K contributors in 2022 and the 10th place is tensorflow/tensorflow with 4.4K contributors. That's really nice, but my guess is that most repositories have very few contributors.

TensorFlow - This one needs no introduction. It’s widely-used and it has several tools and community resources for training and deploying ML/DL models. This one is JS based and I’m not too familiar with JS except that I know a lot of people use it for web development. What I like about it is that it’s used for speech and image recognition. And one of my favorites are text summarization.

If you mean StableHLO, then it has an MLIR dialect: https://github.com/openxla/stablehlo/blob/main/stablehlo/dia....

In the StableHLO spec, we are talking about this in more abstract terms - "StableHLO opset" - to be able to unambiguously reason about the semantics of StableHLO programs. However, in practice the StableHLO dialect is the primary implementation of the opset at the moment.

I wrote "primary implementation" because e.g. there is also ongoing work on adding StableHLO support to the TFLite flatbuffer schema: https://github.com/tensorflow/tensorflow/blob/master/tensorf.... Having an abstract notion of the StableHLO opset enables us to have a source of truth that all the implementations correspond to.

# Download and install ccache for faster compilation wget https://github.com/ccache/ccache/releases/download/v4.8.3/ccache-4.8.3.tar.xz tar -xf ccache-4.8.3.tar.xz pushd ccache-4.8.3 cmake . make -j $CPUS make install popd # Install NumPy, a dependency for PyTorch dnf install -y numpy # Install Python typing extensions for better type-checking sudo -u ec2-user pip3 install typing-extensions # Clone PyTorch repository and install from source git clone --recursive https://github.com/pytorch/pytorch.git pushd pytorch python3 setup.py install popd # Refresh the dynamic linker run-time bindings ldconfig # Install additional Python libraries for PyTorch sudo -u ec2-user pip3 install sympy filelock fsspec networkx

Given how popular they are, these modern designs must appeal to someone, but personally I find them really bad. It's pure form over function with the huge text that reduces my 24" monitor to the information density of a phone and the annoying fade-ins that interfere with quickly skimming the page. This kind of webpage makes me immediately suspicious. I find these landing pages much better: <https://hypothesis.readthedocs.io/en/latest/>, <https://cmocka.org/>, <https://pytorch.org/>.

Large computations in pure Python can also be painfully slow. Luckily, there is a robust landscape of options for numeric processing. The most popular framework is NumPy. There is also PyTorch and other GPU-based tensor processing frameworks.

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

The lack of engagement in the relevant issue trackers (imports, dtypes, I can't actually find an issue for the pooling padding) hurts the legitimate complaints.

- The gain in stable diffusion is modest (15%-25% last I checked?)

- Torch 2.0 only supports static inputs. In actual usage scenarios, this means frequent lengthy recompiles. Eventually, these recompiles will overload the compilation cache and torch.compile will stop functioning.

- Some common augmentations (like TomeSD) break compilation, make it take forever, or kill the performance gains.

- Other miscellaneous bugs (like freezing the Python thread and causing timeouts in web UIs, or errors with embeddings)

- Dynamic input in Torch 2.1 nightly fixes a lot of these issues, but was only maybe working a week ago? See https://github.com/pytorch/pytorch/issues/101228#issuecommen...

- TVM and AITemplate have massive performance gains. ~2x or more for AIT, not sure about an exact number for TVM.

- AIT supported dynamic input before torch.compile did, and requires no recompilation after the initial compile. Also, weights (models and LORAs) can be swapped out without a recompile.

- TVM supports very performant Vulkan inference, which would massively expand hardware compatibility.

Note that the popular SD Web UIs don't support any of this, with two exceptions: VoltaML (with WIP AIT support) and a the Windows DirectML fork of A1111 (which uses optimized ONNX models, I think).

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I found them out myself when making our own implementation of the model. We test our outputs against upstream models. In decoding without history, our tests passed, but in decoding with history there was a mismatch between our implementation and the upstream implementation. Naturally, I assumed that our implementation was wrong (being the newer implementation, not sharing code with theirs), but while debugging this I found that our implementation is actually correct.

Then I was planning to report these issues. Someone else found the causal mask issue a week earlier, so there was no need to report it:

https://github.com/pytorch/pytorch/issues/103082

I reported the issue with rotary embeddings in a discussion of problems that people were running into trying to use KV caching:

https://huggingface.co/tiiuae/falcon-40b/discussions/48#648c...

More in general, I am not sure what the best place is to track these issues. Maybe a model's discussion forums?