guberti commented on code in PR #13815:
URL: https://github.com/apache/tvm/pull/13815#discussion_r1088239878
##########
python/tvm/topi/arm_cpu/mprofile/dsp/micro_kernel/multi_channel_convolve.py:
##########
@@ -179,20 +179,26 @@ def _dual_int16_channel_convolve_impl(_tensor_h,
tensor_w, channels, kernel_h, k
extern "C"
#endif
int32_t {_get_func_name("int16", tensor_w, channels, kernel_h,
kernel_w, suffix)}(
- uint32_t *out,
- uint32_t *tensor,
- uint32_t *kernel) {{
+ int32_t *out,
+ int16_t *tensor,
+ int16_t *kernel) {{
- uint32_t sum_c0 = 0;
- uint32_t sum_c1 = 0;
+ int32_t sum_c0 = 0;
+ int32_t sum_c1 = 0;
+
+ int32_t kernel_i32[{kernel_h} * {kernel_w}];
+ memcpy(kernel_i32, kernel, {kernel_h} * {kernel_w} * 4);
+
+ int32_t tensor_length = {((kernel_w - 1) * (channels // 2) +
(kernel_h - 1) * tensor_w * (channels // 2)) + 1};
Review Comment:
Yep, I agree this formula is correct.
##########
python/tvm/topi/arm_cpu/mprofile/dsp/micro_kernel/multi_channel_convolve.py:
##########
@@ -179,20 +179,26 @@ def _dual_int16_channel_convolve_impl(_tensor_h,
tensor_w, channels, kernel_h, k
extern "C"
#endif
int32_t {_get_func_name("int16", tensor_w, channels, kernel_h,
kernel_w, suffix)}(
- uint32_t *out,
- uint32_t *tensor,
- uint32_t *kernel) {{
+ int32_t *out,
+ int16_t *tensor,
+ int16_t *kernel) {{
- uint32_t sum_c0 = 0;
- uint32_t sum_c1 = 0;
+ int32_t sum_c0 = 0;
+ int32_t sum_c1 = 0;
+
+ int32_t kernel_i32[{kernel_h} * {kernel_w}];
+ memcpy(kernel_i32, kernel, {kernel_h} * {kernel_w} *
sizeof(int32_t));
Review Comment:
I imagine these lines are removed by the compiler (and that the `memcpy`
doesn't actually), but I would still like to confirm there is no performance
regression. I'd also love a comment making it clearer why the `memcpy` doesn't
slow us down like you'd expect.
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