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Ensure XCCL is the default backend for XPU #17

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@Chao1Han Chao1Han commented Jun 5, 2025 •
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When another process group name is registered as the backend for XPU, it immediately replaces XCCL. However, our intention is to designate XCCL as the default distributed backend for XPU.
A simple case is:

import os
import torch
import torch.distributed as dist

dist.Backend.register_backend(
    "fake",
    lambda store, rank, size, timeout: dist.ProcessGroup(rank, size),
    devices=["xpu"],
)

os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "29503"
dist.init_process_group(rank=0, world_size=1)
a = torch.randn(2, device="xpu")
dist.all_reduce(a)

Now this case will raise
RuntimeError: No backend type associated with device type xpu

Test case added in other repo intel/torch-xpu-ops#1721

@Chao1Han Chao1Han closed this Jun 24, 2025
@Chao1Han Chao1Han deleted the xccl-register branch June 24, 2025 05:42
Chao1Han pushed a commit that referenced this pull request Jul 7, 2025
@ahmadsharif1 @pytorchmergebot
Don't call sum() on a tensor that is not summable in layer_norm (py…
899d3d3 
…torch#156600)

Don't call `sum()` on a tensor that is default constructed.

Previously we could call `sum()` on a tensor that was default-contructed. That would lead to an error like this:

```
Traceback (most recent call last):
  File "/home/ahmads/.conda/envs/pt3/lib/python3.12/unittest/case.py", line 58, in testPartExecutor
    yield
  File "/home/ahmads/.conda/envs/pt3/lib/python3.12/unittest/case.py", line 634, in run
    self._callTestMethod(testMethod)
  File "/home/ahmads/.conda/envs/pt3/lib/python3.12/unittest/case.py", line 589, in _callTestMethod
    if method() is not None:
       ^^^^^^^^
  File "/home/ahmads/personal/pytorch/torch/testing/_internal/common_utils.py", line 3191, in wrapper
    method(*args, **kwargs)
  File "/home/ahmads/personal/pytorch/test/test_nn.py", line 7235, in test_layer_norm_backwards_eps
    ln_out_cuda.backward(grad_output_cuda)
  File "/home/ahmads/personal/pytorch/torch/_tensor.py", line 647, in backward
    torch.autograd.backward(
  File "/home/ahmads/personal/pytorch/torch/autograd/__init__.py", line 354, in backward
    _engine_run_backward(
  File "/home/ahmads/personal/pytorch/torch/autograd/graph.py", line 829, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: tensor does not have a device
Exception raised from device_default at /home/ahmads/personal/pytorch/c10/core/TensorImpl.h:1265 (most recent call first):
C++ CapturedTraceback:
#4 std::_Function_handler<std::shared_ptr<c10::LazyValue<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > const> (), c10::SetStackTraceFetcher(std::function<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > ()>)::{lambda()#1}>::_M_invoke(std::_Any_data const&) from Logging.cpp:0
#5 c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) from ??:0
#6 c10::detail::torchCheckFail(char const*, char const*, unsigned int, char const*) from ??:0
#7 at::TensorBase::options() const from :0
#8 at::meta::resize_reduction(at::impl::MetaBase&, at::Tensor const&, c10::OptionalArrayRef<long>, bool, c10::ScalarType, bool) from :0
#9 at::meta::structured_sum_dim_IntList::meta(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from ??:0
#10 at::(anonymous namespace)::wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from RegisterCompositeExplicitAutogradNonFunctional_0.cpp:0
#11 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>), &at::(anonymous namespace)::wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList>, at::Tensor, c10::guts::typelist::typelist<at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType> > >, at::Tensor (at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from RegisterCompositeExplicitAutogradNonFunctional_0.cpp:0
#12 at::_ops::sum_dim_IntList::call(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from ??:0
#13 void at::native::(anonymous namespace)::LaunchGammaBetaBackwardCUDAKernel<float, float>(float const*, float const*, float const*, float const*, long, long, at::Tensor*, at::Tensor*, CUstream_st*) from ??:0
#14 void at::native::(anonymous namespace)::LayerNormBackwardKernelImplInternal<float>(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long, long, at::Tensor*, at::Tensor*, at::Tensor*) from ??:0
#15 at::native::(anonymous namespace)::LayerNormBackwardKernelImpl(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long, long, at::Tensor*, at::Tensor*, at::Tensor*) from ??:0
#16 at::native::layer_norm_backward_cuda(at::Tensor const&, at::Tensor const&, c10::ArrayRef<long>, at::Tensor const&, at::Tensor const&, std::optional<at::Tensor> const&, std::optional<at::Tensor> const&, std::array<bool, 3ul>) from ??:0
#17 at::(anonymous namespace)::(anonymous namespace)::wrapper_CUDA__native_layer_norm_backward(at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, at::Tensor const&, at::Tensor const&, std::optional<at::Tensor> const&, std::optional<at::Tensor> const&, std::array<bool, 3ul>) from RegisterCUDA_0.cpp:0

```

Now we only call `sum(0)` on tensors that are defined and properly guard the `sum(0)` and assignment.
Pull Request resolved: pytorch#156600
Approved by: https://github.com/eqy, https://github.com/ngimel
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@Chao1Han