masahi commented on code in PR #13642:
URL: https://github.com/apache/tvm/pull/13642#discussion_r1058267062
##########
python/tvm/topi/x86/tensor_intrin.py:
##########
@@ -348,3 +348,227 @@ def _instr(index):
binds={data: a_buffer, kernel: b_buffer},
default_buffer_params=buffer_params,
)
+
+
+def dot_32x128x32_u8s8s32_sapphirerapids(LDA):
+ """
+ Int8 dot product by every 16x64 elements using AMX-TMUL Sapphire Rapids
instructions.
+ The tdpxxd instruction takes two tile of uint8 and int8 datatype --
data[16][64] and
+ kernel[1][16][16][4] -- and computes a dot product of data[16][16] in
int32 datatype.
+
+ (Physically, to efficiently leveraging the tile register, we constructing
a 2x2 tiles
+ matmul which performs 32x128x32 in total)
+
+ The pseudo code is as follows:
+ for(k=0; k<2; k++){
+ for(n=0; n<2; n++){
+ tileload64(tmm_b, B)
+ for(m=0; m<2; m++){
+ if(n==0)
+ tileload64(tmm_a, A)
Review Comment:
If you are asking about tensorization in TIR, there is no need to have
separate init, update etc. See
https://github.com/apache/tvm/blob/main/python/tvm/tir/tensor_intrin/x86.py.
The accum buffer is implicitly zero-initialized and we can direct do update via
`+=`.
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