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# Motivation Aligned with other backends, this PR introduces a new API `torch.xpu.is_tf32_supported`, which should be used before `torch.backends.mkldnn.allow_tf32=True` or provide hardware capability information to the Triton # Additional Context On Intel Xe architecture and newer, TF32 operations can be accelerated through DPAS (Dot Product Accumulate Systolic) instructions. Therefore, TF32 support can be determined by checking whether the device supports subgroup matrix multiply-accumulate operations. Pull Request resolved: https://github.com/pytorch/pytorch/pull/163141 Approved by: https://github.com/EikanWang
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torch.xpu
.. automodule:: torch.xpu
.. currentmodule:: torch.xpu
.. autosummary::
:toctree: generated
:nosignatures:
StreamContext
can_device_access_peer
current_device
current_stream
device
device_count
device_of
get_arch_list
get_device_capability
get_device_name
get_device_properties
get_gencode_flags
get_stream_from_external
init
is_available
is_bf16_supported
is_initialized
is_tf32_supported
set_device
set_stream
stream
synchronize
Random Number Generator
.. autosummary::
:toctree: generated
:nosignatures:
get_rng_state
get_rng_state_all
initial_seed
manual_seed
manual_seed_all
seed
seed_all
set_rng_state
set_rng_state_all
Streams and events
.. autosummary::
:toctree: generated
:nosignatures:
Event
Stream
.. automodule:: torch.xpu.memory
.. currentmodule:: torch.xpu.memory
Memory management
.. autosummary::
:toctree: generated
:nosignatures:
empty_cache
max_memory_allocated
max_memory_reserved
mem_get_info
memory_allocated
memory_reserved
memory_stats
memory_stats_as_nested_dict
reset_accumulated_memory_stats
reset_peak_memory_stats
.. toctree::
:hidden:
xpu.aliases.md