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Add support for e2e benchmark for conv2d/conv3d #3329
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3329
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (4 Unrelated Failures)As of commit e2d0395 with merge base e8c4d09 ( BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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Summary:
att, we added this to float8_inference_roofline to reuse code
but we haven't enabled the roofline feature. For now we just
need the e2e speedup time for single conv2d/conv3d against bf16
to understand the speedup expecatation
Also added B200 hardware spec.
Test Plan:
python $SCRIPT_PATH $OUTPUT_FILE \
--recipe_name $RECIPE_NAME \
--shape_gen_name $SHAPE_GEN_NAME \
--M $M --K $K --N $N \
--D $D --H $H --W $W \
--kernel_size $kernel_size \
--op_name conv3d
This doesn't run yet because OSS fbgemm can't be installed in the B200 machine
Reviewers:
Subscribers:
Tasks:
Tags:
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vkuzo
approved these changes
Nov 12, 2025
jainapurva
pushed a commit
that referenced
this pull request
Nov 13, 2025
Summary:
att, we added this to float8_inference_roofline to reuse code
but we haven't enabled the roofline feature. For now we just
need the e2e speedup time for single conv2d/conv3d against bf16
to understand the speedup expecatation
Also added B200 hardware spec.
Test Plan:
python $SCRIPT_PATH $OUTPUT_FILE \
--recipe_name $RECIPE_NAME \
--shape_gen_name $SHAPE_GEN_NAME \
--M $M --K $K --N $N \
--D $D --H $H --W $W \
--kernel_size $kernel_size \
--op_name conv3d
This doesn't run yet because OSS fbgemm can't be installed in the B200 machine
Reviewers:
Subscribers:
Tasks:
Tags:
Co-authored-by: jerryzh <[email protected]>
namgyu-youn
pushed a commit
to namgyu-youn/ao
that referenced
this pull request
Nov 21, 2025
Summary:
att, we added this to float8_inference_roofline to reuse code
but we haven't enabled the roofline feature. For now we just
need the e2e speedup time for single conv2d/conv3d against bf16
to understand the speedup expecatation
Also added B200 hardware spec.
Test Plan:
python $SCRIPT_PATH $OUTPUT_FILE \
--recipe_name $RECIPE_NAME \
--shape_gen_name $SHAPE_GEN_NAME \
--M $M --K $K --N $N \
--D $D --H $H --W $W \
--kernel_size $kernel_size \
--op_name conv3d
This doesn't run yet because OSS fbgemm can't be installed in the B200 machine
Reviewers:
Subscribers:
Tasks:
Tags:
Co-authored-by: jerryzh <[email protected]>
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Summary:
We added this to float8_inference_roofline to reuse code but we haven't enabled the roofline feature. For now we just need the e2e speedup time for single conv2d/conv3d against bf16 to understand the speedup expectation
some if/else branches right now but hopefully we should be able to remove some of these when more roofline support is added for conv2d/conv3d
Also added B200 hardware spec.
Test Plan:
requires fbgemm_gpu_genai nightly and torch nightly:
sample results:
Reviewers:
Subscribers:
Tasks:
Tags: