Fp8 support for item() with cuda, index_select, and fill_ with cpu #128780
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…cpu), and fill_out.
…nto float8-support
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/128780
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (4 Unrelated Failures)As of commit 82f63cf with merge base e3a39d4 ( FLAKY - The following jobs failed but were likely due to flakiness present on trunk:
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pytorch-bot
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module: cpu
mikaylagawarecki
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vkuzo
reviewed
Jun 17, 2024
| @@ -60,7 +60,7 @@ void fill_kernel(TensorIterator& iter, const Scalar& value_scalar) { | |||
| [=]() -> scalar_t { return value; }, | |||
| [=]() { return Vectorized<scalar_t>(value); }); | |||
| }), | |||
| AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX), kBool, AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) | |||
| AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX), kBool, AT_EXPAND(AT_FLOAT8_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) | |||
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is this still needed after https://github.com/pytorch/pytorch/pull/128744/files ?
vkuzo
reviewed
Jun 17, 2024
test/test_torch.py
Outdated
| @@ -3553,7 +3553,9 @@ def test_index_fill(self, device, dtype): | |||
| # FIXME: move to test indexing | |||
| # The test fails for zero-dimensional tensors on XLA | |||
| @onlyNativeDeviceTypes | |||
| @dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16)) | |||
| @dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16, | |||
| torch.float8_e4m3fn, torch.float8_e4m3fnuz, | |||
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optional nit: maybe save the float8 types in a list and unpack everywhere? will make it easier for future op support PRs to not forget to handle all of them
|
looks great, thanks! Just had one question inline on whether the fill code changes are still needed. |
eqy
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Jun 17, 2024
eqy
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Fixes #128370.
Fixes #128257.
Added fp8 support for item with cuda, index_select, and fill_ with cpu.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10