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cpu: aarch64: Add bf16 Datatype Support and Relax BenchDNN Threshold for AArch64 SoftMax #1950
cpu: aarch64: Add bf16 Datatype Support and Relax BenchDNN Threshold for AArch64 SoftMax #1950
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…nchDNN threshold for softmax on AArch64
src/cpu/aarch64/jit_uni_softmax.hpp
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&& ((src_dt == bf16 || dst_dt == bf16) ? mayiuse_bf16() | ||
: true) |
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Nit:
&& ((src_dt == bf16 || dst_dt == bf16) ? mayiuse_bf16() | |
: true) | |
&& IMPLICATION(utils::one_of(bf16, src_dt, dst_dt), mayiuse_bf16()) |
And same goes to backward.
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Hi @dzarukin, I have made the changes as per your suggestion in both the forward and backward SoftMax kernel initializations.
Thanks for your contribution. I'm not going to block it going in, but why is the relaxed accuracy required for bf16 softmax? We needed it for f16 because the ACL implementation does accumulation in f16 and the instructions naturally accumulate to f16. But as far as I know, all existing AArch64 bf16 instructions accumulate to f32, which is what oneDNN already expects. |
HI @jondea, thank you for your feedback. There is some accuracy drop in the JIT SoftMax bf16 implementation compared to f32. BF16 benchDNN test cases were passing before commit ID: 6727bbe, but after this commit, a few test cases are failing for bf16. Hence, I modified the accuracy to be more relaxed. |
How big was the drop in accuracy, and is it expected? The existing comment under this ifdef only talks about a temporary fix for f16 (which we are planning to remove to use the accumulation mode). Could please you add a comment explaining why it is necessary for bf16 as well, and why it is different for other platforms (non-AArch64). |
The accuracy drop is under 0.0078125 threshold for BF16 |
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Just this one please
Could please you add a comment explaining why it is necessary for bf16 as well, and why it is different for other platforms (non-AArch64).
@@ -232,7 +232,7 @@ void setup_cmp(compare::compare_t &cmp, const prb_t *prb, data_kind_t kind, | |||
const float trh_coeff_bwd = (prb->dir & FLAG_FWD) ? 1.f : 4.f; | |||
const float trh_f32 = trh_coeff_log * trh_coeff_bwd * trh_coeff_f32 | |||
* epsilon_dt(trh_dt); | |||
#if DNNL_AARCH64_USE_ACL || defined(DNNL_SYCL_HIP) | |||
#if DNNL_AARCH64 || defined(DNNL_SYCL_HIP) | |||
// MIOpen and ACL softmax accumulate in F16, but oneDNN now expects accumulation in |
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This comment needs updating to mention bf16 too
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Comment updated.
Hi @jondea, the reason for this is that there is a minor accuracy drop observed in the JIT SoftMax bf16 implementation compared to f32. This accuracy drop is within the acceptable threshold of 0.0078125 for bf16. This adjustment is specific to AArch64 due to its unique handling of bf16 instructions, which accumulate in f32. The other platforms (non-AArch64) do not exhibit the same behavior with bf16 instructions; hence the threshold relaxation is not necessary for them. |
Ah, this appears to have failed clang format, I didn't spot that sorry. |
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@deepeshfujitsu, for your next PRs please make sure commit messages are formatted according to Contributing Guidelines. |
Sure @vpirogov, I will take care |
Description
This pull request includes the following changes:
1. Add bf16 Datatype Support:
2. Update BenchDNN Threshold for AArch64:
These changes aim to enhance the compatibility and accuracy of SoftMax operations on AArch64 with bf16 datatype support.
Checklist
General
make test
andmake test_benchdnn_*
) pass locally for each commit? Yesmake test
./gtests/test_softmax
./benchdnn --softmax --batch=inputs/softmax/test_softmax_all