[ROCm] Improve reduction sum performance #160466
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* Use input vectorization for reduction_on_fastest_striding_dimension when dim >= 0
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/160466
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit bff4a8a with merge base 4d419a7 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
pytorch-bot
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added
module: rocm
jerrymannil
added a commit
to ROCm/pytorch
that referenced
this pull request
Aug 12, 2025
* 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
jeffdaily
approved these changes
Aug 12, 2025
jeffdaily
added
release notes: rocm
pruthvistony
pushed a commit
to ROCm/pytorch
that referenced
this pull request
Aug 13, 2025
* 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
|
@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
jerrymannil
added a commit
to ROCm/pytorch
that referenced
this pull request
Aug 13, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
dhonnappa-amd
pushed a commit
to ROCm/pytorch
that referenced
this pull request
Aug 13, 2025
* 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
chuanhaozhuge
pushed a commit
that referenced
this pull request
Aug 14, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
Pull Request resolved: #160466
Approved by: https://github.com/petrex, https://github.com/jeffdaily
pruthvistony
pushed a commit
to ROCm/pytorch
that referenced
this pull request
Aug 15, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
chuanhaozhuge
pushed a commit
that referenced
this pull request
Aug 18, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
Pull Request resolved: #160466
Approved by: https://github.com/petrex, https://github.com/jeffdaily
can-gaa-hou
pushed a commit
to can-gaa-hou/pytorch
that referenced
this pull request
Aug 22, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
Pull Request resolved: pytorch#160466
Approved by: https://github.com/petrex, https://github.com/jeffdaily
jerrymannil
added a commit
to ROCm/pytorch
that referenced
this pull request
Sep 5, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
markc-614
pushed a commit
to markc-614/pytorch
that referenced
this pull request
Sep 17, 2025
* Use input vectorization for reduction_on_fastest_striding_dimension when dim0 >= 128
**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
Pull Request resolved: pytorch#160466
Approved by: https://github.com/petrex, https://github.com/jeffdaily
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Reproducer:
Before (MI300X):
Avg time for shape (5079670, 128): 1629.99 us
After (MI300X)
Avg time for shape (5079670, 128): 1008.59 us
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd