mirror of https://github.com/llvm/torch-mlir
78e10ff09b
Even though the reference compiler is not about performance, inlining
the generated sparse helper methods has a rather big positive impact on
performance, leaving a much better first impression. Therefore, we added
this inlining pass (which leaves all other PyTorch modules unaffected,
since they tend to be one big main() method to start with).
testing:
$./tools/e2e_test.sh --config linalg
Summary:
Passed: 1164
Expectedly Failed: 8
$ python -m e2e_testing.main --config=torchdynamo
Summary:
Passed: 976
Expectedly Failed: 162
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| .. | ||
| jit_ir_common | ||
| ltc | ||
| onnx_c_importer | ||
| pt1 | ||
| CMakeLists.txt | ||