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Replace Linux A10 pools with A100 - #23547

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Changming Sun (snnn) merged 10 commits into
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snnn/replace_pool
Feb 19, 2025
Merged

Replace Linux A10 pools with A100#23547
Changming Sun (snnn) merged 10 commits into
mainfrom
snnn/replace_pool

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Description

The latest driver for Linux A10 machines is 535.
The latest driver for Windows A10 machines is 550.
We have some unused A100 quota. If we can replace the A10 machines with A100 machines, we might be able to upgrade our CUDA from 12.2 to 12.4.

Motivation and Context

@snnn
Changming Sun (snnn) marked this pull request as ready for review February 19, 2025 21:03
@snnn
Changming Sun (snnn) merged commit 4dff07d into main Feb 19, 2025
@snnn
Changming Sun (snnn) deleted the snnn/replace_pool branch February 19, 2025 21:04
Guenther Schmuelling (guschmue) pushed a commit that referenced this pull request Mar 6, 2025
### Description
The latest driver for Linux A10 machines is 535.
The latest driver for Windows A10 machines is 550.
We have some unused A100 quota. If we can replace the A10 machines with
A100 machines, we might be able to upgrade our CUDA from 12.2 to 12.4.
Ashrit Shetty (ashrit-ms) pushed a commit that referenced this pull request Mar 17, 2025
### Description
The latest driver for Linux A10 machines is 535.
The latest driver for Windows A10 machines is 550.
We have some unused A100 quota. If we can replace the A10 machines with
A100 machines, we might be able to upgrade our CUDA from 12.2 to 12.4.
Tianlei Wu (tianleiwu) added a commit that referenced this pull request Jan 5, 2026
Update GQA benchmark to support bfloat16 and default to testing the
first configuration (fast mode).

Note that test_sparse_attention.py was removed in
#23547. It is referenced by
the benchmark script, so I add it back and disable the test in pipeline
mode.

Example output from H200 GPU:
```
prompt-sm90-Llama3-8B-b1-h32_8x128-float16:
   sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0             16.0       0.781751                 0.571226
1             32.0       0.893813                 0.684198
2             64.0       1.434056                 1.589263
3            128.0       1.142192                 1.681969
4            256.0       1.503483                 2.225498
5            512.0       1.045732                 1.878660
6           1024.0       2.334924                 0.916745
7           2048.0       2.229924                 3.001290
8           4096.0       4.309678                 3.198855
9           8192.0       7.932211                 7.910411

token-sm90-Llama3-8B-b1-h32_8_d128-float16:
   past_sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0                  16.0       1.751966                 0.780081
1                  32.0       1.302806                 0.043939
2                  64.0       2.301024                 2.207282
3                 128.0       2.294556                 3.010107
4                 256.0       2.931330                 1.781768
5                 512.0       1.210220                 2.799579
6                1024.0       2.767142                 2.660434
7                2048.0       1.420229                 0.091433
8                4096.0       0.860655                 0.801022
9                8191.0       0.749525                 0.820858

prompt-sm90-Llama3-8B-b1-h32_8x128-bfloat16:
   sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0             16.0       1.085427                 0.666664
1             32.0       1.714795                 0.931262
2             64.0       1.729093                 1.438733
3            128.0       1.071263                 2.486135
4            256.0       1.957349                 1.342417
5            512.0       1.159680                 1.591321
6           1024.0       0.743702                 2.035150
7           2048.0       1.452736                 1.788801
8           4096.0       4.029917                 4.041565
9           8192.0       7.934485                 7.931600

token-sm90-Llama3-8B-b1-h32_8_d128-bfloat16:
   past_sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0                  16.0       0.044354                 0.043983
1                  32.0       0.040715                 0.044061
2                  64.0       0.045586                 0.044071
3                 128.0       0.062204                 0.061418
4                 256.0       0.074764                 4.874854
5                 512.0       2.472094                 2.102259
6                1024.0       4.911269                 1.396149
7                2048.0       4.898032                 1.684034
8                4096.0       2.523432                 2.192279
9                8191.0       1.651366                 3.427370
```
alex-spacemit pushed a commit to spacemit-com/onnxruntime that referenced this pull request Jan 20, 2026
Update GQA benchmark to support bfloat16 and default to testing the
first configuration (fast mode).

Note that test_sparse_attention.py was removed in
microsoft#23547. It is referenced by
the benchmark script, so I add it back and disable the test in pipeline
mode.

Example output from H200 GPU:
```
prompt-sm90-Llama3-8B-b1-h32_8x128-float16:
   sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0             16.0       0.781751                 0.571226
1             32.0       0.893813                 0.684198
2             64.0       1.434056                 1.589263
3            128.0       1.142192                 1.681969
4            256.0       1.503483                 2.225498
5            512.0       1.045732                 1.878660
6           1024.0       2.334924                 0.916745
7           2048.0       2.229924                 3.001290
8           4096.0       4.309678                 3.198855
9           8192.0       7.932211                 7.910411

token-sm90-Llama3-8B-b1-h32_8_d128-float16:
   past_sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0                  16.0       1.751966                 0.780081
1                  32.0       1.302806                 0.043939
2                  64.0       2.301024                 2.207282
3                 128.0       2.294556                 3.010107
4                 256.0       2.931330                 1.781768
5                 512.0       1.210220                 2.799579
6                1024.0       2.767142                 2.660434
7                2048.0       1.420229                 0.091433
8                4096.0       0.860655                 0.801022
9                8191.0       0.749525                 0.820858

prompt-sm90-Llama3-8B-b1-h32_8x128-bfloat16:
   sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0             16.0       1.085427                 0.666664
1             32.0       1.714795                 0.931262
2             64.0       1.729093                 1.438733
3            128.0       1.071263                 2.486135
4            256.0       1.957349                 1.342417
5            512.0       1.159680                 1.591321
6           1024.0       0.743702                 2.035150
7           2048.0       1.452736                 1.788801
8           4096.0       4.029917                 4.041565
9           8192.0       7.934485                 7.931600

token-sm90-Llama3-8B-b1-h32_8_d128-bfloat16:
   past_sequence_length  ORT-GQA-Dense  ORT-GQA-Dense-PackedQKV
0                  16.0       0.044354                 0.043983
1                  32.0       0.040715                 0.044061
2                  64.0       0.045586                 0.044071
3                 128.0       0.062204                 0.061418
4                 256.0       0.074764                 4.874854
5                 512.0       2.472094                 2.102259
6                1024.0       4.911269                 1.396149
7                2048.0       4.898032                 1.684034
8                4096.0       2.523432                 2.192279
9                8191.0       1.651366                 3.427370
```
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2 participants