mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
8b6a78f39f
This PR allows user to author a CUDA kernel in python.
```
from torch.cuda.jiterator import create_jit_fn
code_string = "template <typename T> T my_kernel(T x, T y, T alpha) { return -x * y + x - y + alpha; }"
jitted_fn = create_jit_fn(code_string, alpha=0)
a = torch.rand(3, device='cuda')
b = torch.rand(3, device='cuda')
result = jitted_fn(a, b, alpha=1.0)
```
Limitations:
- Only supports elementwise kernel
- 1~8 tensor inputs (empty input, e.g. factory methods, is not supported)
- inputs tensors must live in cuda device
- cpu Scalar is not supported
- kwargs must be pre-declared when calling create_jit_fn
- kwargs must be convertible to at::Scalar, one of float64, int64_t, bool. (complex not support for now)
TODOs:
- [x] consolidate union and c10::variant implementation
- [x] plug into existing op testing framework
- [ ] rename files, place files in the right folder
- [ ] place util functions in the right file
- [x] enforce assumptions in python interface e.g <8 inputs, kwargs types
- [x] Add user-facing documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76394
Approved by: https://github.com/mruberry
134 lines
2.4 KiB
ReStructuredText
134 lines
2.4 KiB
ReStructuredText
torch.cuda
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===================================
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.. automodule:: torch.cuda
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.. currentmodule:: torch.cuda
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.. autosummary::
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:toctree: generated
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:nosignatures:
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StreamContext
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can_device_access_peer
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current_blas_handle
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current_device
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current_stream
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default_stream
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device
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device_count
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device_of
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get_arch_list
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get_device_capability
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get_device_name
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get_device_properties
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get_gencode_flags
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get_sync_debug_mode
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init
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ipc_collect
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is_available
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is_initialized
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memory_usage
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set_device
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set_stream
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set_sync_debug_mode
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stream
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synchronize
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utilization
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Random Number Generator
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-------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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get_rng_state
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get_rng_state_all
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set_rng_state
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set_rng_state_all
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manual_seed
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manual_seed_all
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seed
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seed_all
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initial_seed
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Communication collectives
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-------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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comm.broadcast
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comm.broadcast_coalesced
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comm.reduce_add
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comm.scatter
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comm.gather
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Streams and events
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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Stream
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ExternalStream
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Event
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Graphs (beta)
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-------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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graph_pool_handle
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CUDAGraph
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graph
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make_graphed_callables
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Memory management
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-----------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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empty_cache
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list_gpu_processes
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mem_get_info
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memory_stats
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memory_summary
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memory_snapshot
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memory_allocated
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max_memory_allocated
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reset_max_memory_allocated
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memory_reserved
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max_memory_reserved
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set_per_process_memory_fraction
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memory_cached
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max_memory_cached
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reset_max_memory_cached
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reset_peak_memory_stats
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caching_allocator_alloc
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caching_allocator_delete
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.. FIXME The following doesn't seem to exist. Is it supposed to?
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https://github.com/pytorch/pytorch/issues/27785
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.. autofunction:: reset_max_memory_reserved
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NVIDIA Tools Extension (NVTX)
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-----------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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nvtx.mark
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nvtx.range_push
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nvtx.range_pop
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Jiterator (beta)
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-----------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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jiterator._create_jit_fn
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