Add FP32 and Bias to fulfill the functionalities required by torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION
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GPU reference code path causes segfaults.
This allows users to select device to compute reference results without modifying the source file.
Example usage:
```
DB=v2python/rules/tuning_database.sqlite3
python -m v2python.table_tool -k '' --action dumpcsv \
-f $DB --table_name 'FLASH$attn_fwd' \
--table_file 'attn_fwd.fp32mi300.csv' \
--select_where 'inputs$Q_dtype = "torch.float32"'
git checkout another_branch -- $DB
python -m v2python.table_tool -k '' --action loadcsv \
-f $DB --table_name 'FLASH$attn_fwd' \
--table_file attn_fwd.fp32mi300.csv \
--ignore_id
```
Note: --ignored_id does not support cases that 'id' is not the first
column of the CSV file, for simplicity.
If .expand() is called upon a tensor, the result tensor may have one or more zeros in its stride()
Otherwise the dk result is incorrect for fp32
1. Computed gradients were not stored for future use, which basically means the gradients were not tested at all. 2. bias should be created as a Rank 2 tensor and then expand to Rank 4.
groenenboomj
reviewed
May 2, 2024
groenenboomj
approved these changes
May 2, 2024
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This PR includes the following major changes
fp32datatype support, and the corresponding tuning database informationtable_tool.pyto partially dump/load.csvfile, allowing database mergingKnown limitations:
.expand()-ed ones are unlikely to work) for backward direction. No checking is performed on this requisite and failure may be silenttest_forward.pyis still using the old method to estimate ATOL/RTOL