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Fused attention instances & padding tests (#395)

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* modify comment

* trim unnecessary check

* add gemm spec in kernel name

* add TNTT gemm_gemm + atten kernel instances

* refactor attention padding to better fit in unit tests

This streamlines usage where "ResetNaNToMinusInf" is now hidden from user facing device op.
Also added compile-time conditionals that load OOB value as NaN only after padding is enabled

* add adhoc padding test for atten

* shrink input value range for attention kernel validation to avoid occasional error by 1e-3

Still unsure whether this kind of deterministic floating point accurary issue is expected
or not. May want to try exact same approach as the GPU kernel in the host reference
GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then,
shrink the input value range as it is less likely to produce errors of around ~1e-3.

* attention kernel proper granular padding for all 4 dims

* IsSupportedArgument checks

* test more padded cases

* block PadK specialization in attention kernels

* workaround clang crash for gfx908

(gfx908 only) workaround for compiler crash in fused kernels on mainline #9110; #10738 seems ok
error message was "fatal error: error in backend: Error while trying to spill VGPR0 from class
VGPR_32: Cannot scavenge register without an emergency spill slot!"
this fall back to less ideal way of handle NPadding in fused attention kernel

* comment out kernels giving wrong results on MI100; MI200 doesn't seem affected

[ROCm/composable_kernel commit: 868e5c555b]
This commit is contained in:
Anthony Chang
2022-09-07 03:38:56 +08:00
committed by GitHub
parent 5a11d4edc7
commit e078585f04
15 changed files with 540 additions and 495 deletions
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5
example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt
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@@ -1,3 +1,8 @@
add_example_executable(example_batched_gemm_scale_softmax_gemm_xdl_fp16 batched_gemm_scale_softmax_gemm_xdl_fp16.cpp)
add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_xdl_fp16 batched_gemm_scale_softmax_gemm_permute_xdl_fp16.cpp)
add_example_executable(example_padded_batched_gemm_scale_softmax_gemm_xdl_fp16 padded_batched_gemm_scale_softmax_gemm_xdl_fp16.cpp)
add_custom_target(example_batched_gemm_scale_softmax_gemm)
add_dependencies(example_batched_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_xdl_fp16)
add_dependencies(example_batched_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_permute_xdl_fp16)
add_dependencies(example_batched_gemm_scale_softmax_gemm example_padded_batched_gemm_scale_softmax_gemm_xdl_fp16)

7
example/32_batched_gemm_scale_softmax_gemm/padded_batched_gemm_scale_softmax_gemm_xdl_fp16.cpp
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@@ -49,14 +49,9 @@ using B0Layout = Col;
using B1Layout = Row;
using CLayout = Row;
// When using padded DeviceBatchedGemmSoftmaxGemm_Xdl_CShuffle kernel, 2 specs should be set:
// 1. GemmSpecialization should be set to MNPadding(or NPadding in future)
// 2. Acc0ElementOp should be set to ScaleAndResetNaNToMinusInfinity
// Otherwise, wrong result may be produced.
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = ck::tensor_operation::element_wise::ScaleAndResetNaNToMinusInfinity;
using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;