[TIR] Refactor BF16Legalize#14405
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junrushao merged 1 commit intoapache:mainfrom Mar 28, 2023
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This PR refactors BF16Legalize to enable more f32 computations. We also split the BF16Legalize into two steps. - BF16ComputeLegalize changes all computation to f32 while keeping the external BF16 storages. - BF16StorageLegalize changes all storage to u16. Now BF16 kernels accept tvm.nd.array that are created as bfloat16 type.
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note: the android failure is not related to this PR. |
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cc @vinx13 |
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Retriggering failed Android tests |
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Mar 28, 2023
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| // TODO(tvm-team): consider add native support | ||
| ICHECK(!from.is_bfloat16()) << "BF16 needs to be storaged lowered first"; | ||
| ICHECK(!to.is_bfloat16()) << "BF16 needs to be storaged lowered first"; | ||
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seems possible to support software BF16 in upstream LLVM/MLIR world. leaving it to future work
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Good point, already left as a todo
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| # handle the bfloat16 so we explicitly allocate | ||
| # bfloat16 arrays as input | ||
| for i, param in enumerate(mod["main"].params): | ||
| if param.type_annotation.dtype == "bfloat16": | ||
| input_data[i] = tvm.nd.empty(input_data[i].shape, "bfloat16").copyfrom( | ||
| input_data[i] | ||
| ) |
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why are we adding ONNX tests in this PR though?
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This is needed to patch up onnx converting bf16 to uint16
junrushao
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Mar 28, 2023
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This PR refactors BF16Legalize to enable more f32 computations.
We also split the BF16Legalize into two steps.
the external BF16 storages.
Now BF16 kernels accept tvm.nd.array that are created as bfloat16 type.