- 02 Jul, 2022 1 commit
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Paul authored
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- 01 Jul, 2022 1 commit
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Paul authored
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- 26 Jun, 2022 4 commits
- 25 Jun, 2022 3 commits
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Paul authored
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Paul authored
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Paul Fultz II authored
* Jit contiguous
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- 22 Jun, 2022 1 commit
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Ted Themistokleous authored
Updated each source file in the repo with the existing license.
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- 10 Jun, 2022 1 commit
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Paul Fultz II authored
Consolidate the vectorize and preload Add vectorization to reduction Co-authored-by:kahmed10 <15948690+kahmed10@users.noreply.github.com>
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- 04 Jun, 2022 2 commits
- 31 May, 2022 1 commit
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Paul authored
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- 25 May, 2022 2 commits
- 24 May, 2022 1 commit
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Paul Fultz II authored
Remove std references in runtime compilation since these are not available when using hiprtc and the headers may not be available on the system
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- 23 May, 2022 2 commits
- 20 May, 2022 2 commits
- 19 May, 2022 1 commit
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Paul authored
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- 17 May, 2022 2 commits
- 12 May, 2022 1 commit
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Paul authored
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- 11 May, 2022 4 commits
- 10 May, 2022 2 commits
- 09 May, 2022 1 commit
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Paul Fultz II authored
Improves performance for add_gelu. In bert it is 4x faster and for mul_add it is 50% faster than what we current have.
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- 06 May, 2022 1 commit
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Chris Austen authored
Move to CI containers to rocm 5.0.2 upgrade to 20.04 free up some more file space in github action environments
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- 03 May, 2022 4 commits
- 29 Apr, 2022 1 commit
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turneram authored
Add ref and gpu implementations for ONNX op GatherND Resolves #1032
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- 27 Apr, 2022 1 commit
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Paul Fultz II authored
With reductions such as {2048, 2, 1456} on axes 1, this is 23x faster than using our new block_reduce, and its even over 100x faster than our original reduce_sum: # lane gpu::code_object[code_object=13736,symbol_name=kernel,global=2981888,local=1024,]: 0.0672928ms # block gpu::code_object[code_object=13800,symbol_name=kernel,global=39321600,local=64,]: 1.46072ms # original gpu::reduce_sum[axes={1}]: 6.73456ms There is some basic logic to pick between lane and block reduce automatically.
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- 17 Apr, 2022 1 commit
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Paul Fultz II authored
There is significant improvement on larger tensors with half almost 50% faster: lens: [1024, 384, 768] gpu::code_object[code_object=13832,symbol_name=kernel,global=39321600,local=256,]: 1.16685ms gpu::reduce_sum[axes={2}]: 1.73126ms Also for non-trivial layouts this can sometimes be over 2x faster: lens: [64, 1024, 768, 4] gpu::code_object[code_object=13832,symbol_name=kernel,global=39321600,local=256,]: 1.1706ms gpu::reduce_sum[axes={1}]: 2.63375ms Of course if the stride becomes larger this speed improvement diminishes due to poor memory access patterns. A lane_reduce instead of a block_reduce is needed for such type of kernels. I plan to address that in a future PR. Finally, this also includes a MIGRAPHX_GPU_DUMP_ASM env variable which will print out the assembly when the kernel compiles.
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