mengceng15 opened a new pull request, #11699:
URL: https://github.com/apache/tvm/pull/11699
This PR is a minor fix on the AutoTVM int8 vnni dense task extraction.
VNNI dense strategy is used only when the input-weight-out datatypes are
u8s8s32 and the weight layout is NC16n4c.
Without altering the weight layout, for a simple dense workload:
def @main(%data: Tensor[(1, 16), uint8] /* ty=Tensor[(1, 16), uint8] /,
%weight: Tensor[(32, 16), int8] / ty=Tensor[(32, 16), int8] /) -> Tensor[(1,
32), int32] {
nn.dense(%data, %weight, units=None, out_dtype="int32") / ty=Tensor[(1, 32),
int32] */
}
There are two non-VNNI tasks extracted:
[Task(func_name=dense_nopack.x86, args=(('TENSOR', (1, 16), 'uint8'),
('TENSOR', (32, 16), 'int8'), None, 'int32'), kwargs={},
workload=('dense_nopack.x86', ('TENSOR', (1, 16), 'uint8'), ('TENSOR', (32,
16), 'int8'), None, 'int32')),
Task(func_name=dense_pack.x86, args=(('TENSOR', (1, 16), 'uint8'),
('TENSOR', (32, 16), 'int8'), None, 'int32'), kwargs={},
workload=('dense_pack.x86', ('TENSOR', (1, 16), 'uint8'), ('TENSOR', (32, 16),
'int8'), None, 'int32'))]
WIth this fix, one VNNI task is extracted:
[Task(func_name=dense_vnni.x86, args=(('TENSOR', (1, 16), 'uint8'),
('TENSOR', (2, 4, 16, 4), 'int8'), None, 'int32'), kwargs={},
workload=('dense_vnni.x86', ('TENSOR', (1, 16), 'uint8'), ('TENSOR', (2, 4, 16,
4), 'int8'), None, 'int32'))]
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