mirror of https://github.com/llvm/torch-mlir
6ff71b40c8
This adds support for converting DynamicQuantizeLinear from torch-onnx to torch. I could not get an e2e test to pass, since there seems to be some issues with uint8 casting somewhere lower in the pipeline. For example compiling with IREE for llvm-cpu, I would get either the correct zero point (if zp < 128) or the correct zero-point minus 256 (if zp >= 128). The output tensor seems to always return a tensor of zeros, which also occurs when running uint8 examples through QuantizeLinear. Edit: the first problem can be resolved by casting the output back to uint8 on output, the second problem is resolved with PR #3018 |
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| .. | ||
| CAPI | ||
| Conversion | ||
| Dialect | ||
| RefBackend | ||
| python | ||
| CMakeLists.txt | ||
| lit.cfg.py | ||
| lit.site.cfg.py.in | ||