[AUTOGENERATED] [release/2.8] [ROCm] Improve reduction sum performance #2505
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* Use input vectorization for reduction_on_fastest_striding_dimension
when dim0 >= 0
**Reproducer:**
```
import time
import torch
shapes = [
(5079670, 128)
]
dims = [
(1)
]
for i, shape in enumerate(shapes):
x = torch.randn(shape, device='cuda', dtype=torch.float)
for _ in range(10):
w = torch.sum(x, dims[i])
torch.cuda.synchronize()
print(w.size())
start_time = time.time()
for _ in range(50):
_ = torch.sum(x, dims[i])
torch.cuda.synchronize()
end_time = time.time()
mean_time = (end_time - start_time)/50
print(f"Avg time for shape {shape}: {mean_time * 1e6:.2f} us")
```
**Before (MI300X):**
Avg time for shape (5079670, 128): 1629.99 us
**After (MI300X)**
Avg time for shape (5079670, 128): 1008.59 us
cherry-pick of pytorch#160466
Fixes SWDEV-546136
|
Jenkins build for b31274f527974938f03d4f4b5e9375c3345154e1 commit finished as FAILURE |
pruthvistony
approved these changes
Aug 15, 2025
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Cherry-pick of #2492