manupa-arm commented on issue #8717:
URL: https://github.com/apache/tvm/issues/8717#issuecomment-914445825
Thanks for the investigation and the suggestions seems to improve the things
a bit.
1.) Would it be possible to share the TIR Primfunc that is the source for
this code generation ?
2.) In the first example, it seems we are unnecessarily maintaining a
feature map of int32 just to do that in another set of loops -- even with int16
cast.
```
for (int32_t ax0_ax1_fused_ax2_fused_ax3_fused = 0;
ax0_ax1_fused_ax2_fused_ax3_fused < 401408;
++ax0_ax1_fused_ax2_fused_ax3_fused) {
int32_t _1 = (int32_t)(((((0 != 0) ?
(((int64_t)(((int32_t*)DepthwiseConv2d)[(ax0_ax1_fused_ax2_fused_ax3_fused)] +
((int32_t*)placeholder2)[((ax0_ax1_fused_ax2_fused_ax3_fused & 127))])) <<
((int64_t)0)) :
((int64_t)(((int32_t*)DepthwiseConv2d)[(ax0_ax1_fused_ax2_fused_ax3_fused)] +
((int32_t*)placeholder2)[((ax0_ax1_fused_ax2_fused_ax3_fused & 127))]))) *
(int64_t)2080045879) + ((int64_t)1 << ((int64_t)((4 + 31) - 1)))) >>
((int64_t)(4 + 31)));
int32_t _2 = (_1) < (255) ? (_1) : (255);
((int16_t*)T_cast)[(ax0_ax1_fused_ax2_fused_ax3_fused)] =
((int16_t)((uint8_t)((_2) > (0) ? (_2) : (0))));
}
```
Do we know why HW feature dictates that whole feature map needs to be int16
(as opposed to doing a cast on the fly -- fused) -- especially in this case,
int16 tensor bleeding out the fused primitive relay operator that is not used
until the next operator ?
My question why we cant the consuming operator create int16 just at the
accumulator without needing the feature map wide int16 tensor.
cc: @anijain2305
3.) In the second example, can we get the fusion done as well ?
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