mirror of https://github.com/llvm/torch-mlir
43 lines
2.3 KiB
Markdown
43 lines
2.3 KiB
Markdown
# mnist-playground
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This is intended to be a short-lived "playground" for doing various experiments, guided by a real model use case, for improving the npcomp reference backend.
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It's expected that utilities developed here will graduate to a more general utility or that this utility will be obsoleted by Python-driven flows once those come online.
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## Goals:
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- Obtain a performance-grounded analysis of the TCF/TCP design + reference backend design, and improve the designs.
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- Make forward progress on TCF/TCP + reference backend while the PyTorch frontend is being brought up.
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## Rough sketch of how we intend to get there:
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1. Link against PyTorch, and write a simple routine to do inference on a simple FC MNIST.
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2. Write a similar routine in TCF, extending TCF and the reference backend as needed for functional completeness. The PyTorch code serves as a numerical correctness reference.
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3. Run and profile the reference backend and obtain a set of action items for design improvements, both to performance and stability. The PyTorch code serves as a performance baseline.
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4. Implement important action items on a priority basis, and document remaining major design issues that don't make sense to address at this time, along with a justification for why the current design doesn't prevent us from eventually solving them. Iterate the previous step and this one as makes sense.
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5. (Stretch) Add support for convolutional MNIST and/or training.
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## Current Status
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Step 1. DONE
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Step 2. MOSTLY DONE. Still need to improve the op set to make the FC MNIST more complete. In particular, implementing functionality for reshape and softmax.
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Step 3. STARTING. Initial performance on 10x784x100 (10 FC feature, batch 100) is 66x off from PyTorch. No profiling done yet.
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Example command line (the .mlir file and `-invoke` are similar to npcomp-run-mlir):
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```
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$ mnist-playground tools/mnist-playground/fc.mlir -invoke fc
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PyTorch: numRuns: 16384 nsPerRun: 3.947563e+05
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RefE2E: numRuns: 256 nsPerRun: 2.471073e+07
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Ratio (RefE2E / PyTorch): 62.5974
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```
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There is currently a fragile dependency between hardcoded `at::` function calls in the .cpp file and the TCF code in the `.mlir` file. A correctness check is done to make sure they agree. Once we have a PyTorch frontend and/or ATen roundrip ATen backend oneline, we can avoid this fragility.
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