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will-cromar
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Sep 5, 2024
| XLATensorPtr xmask = bridge::GetXlaTensor(mask); | ||
| auto masked_input = tensor_methods::mul(xinput, xmask); | ||
| auto result = tensor_methods::all_reduce(masked_input, AllReduceType::kSum, | ||
| 1.0, {}, true); |
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nit: name the non-obvious arguments at the end here. Assuming these two are scale and replica groups, /*scale=*/1, /*groups=*/{} (double check the names).
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| @absltest.skipIf(lambda: tpu.num_logical_cores_per_chip() >= 2, | ||
| @absltest.skipIf(tpu.num_logical_cores_per_chip() >= 2, |
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| # "broadcast(Tensor self, int src, str tag, int[] ranks, int group_size) -> Tensor", | ||
| @torch.library.impl("_c10d_functional::broadcast", "XLA") |
| at::Tensor mask; | ||
| const torch::lazy::BackendDevice& device = xinput->GetDevice(); | ||
| if (device.ordinal() == src) { | ||
| mask = at::ones_like(input); |
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Is there an equivalent to torch.no_grad() in C++? That's the only difference I see between the original python version and this one
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Searched the doc and we can use the following scope for tensor operation without grad:
{
at::NoGradGuard no_grad;
// tensor operations
}
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Anyone knows why we set no grad here:
@JackCaoG
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Support torch.distributed.broadcast for both dynamo and nondynamo.
This PR needs pytorch/pytorch#135171 to be merged first.