[CUDA] stable diffusion benchmark allows IO binding for optimum #22834
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
![github-actions[bot]](https://avatars.githubusercontent.com/in/15368?s=60&v=4){kind=small}
onnxruntime/python/tools/transformers/models/stable_diffusion/benchmark.py
Outdated
Show resolved
Hide resolved
onnxruntime/python/tools/transformers/models/stable_diffusion/benchmark.py
Outdated
Show resolved
Hide resolved
|
We should upgrade the Optimum version here once those changes are merged. |
kunal-vaishnavi
approved these changes
Nov 14, 2024
guschmue
pushed a commit
that referenced
this pull request
Dec 2, 2024
### Description Update stable diffusion benchmark: (1) allow IO binding for optimum. (2) do not use num_images_per_prompt across all engines for fair comparison. Example to run benchmark of optimum on stable diffusion 1.5: ``` git clone https://github.com/tianleiwu/optimum cd optimum git checkout tlwu/diffusers-io-binding pip install -e . pip install -U onnxruntime-gpu git clone https://github.com/microsoft/onnxruntime cd onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion git checkout tlwu/benchmark_sd_optimum_io_binding pip install -r requirements/cuda12/requirements.txt optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 --task text-to-image ./sd_onnx_fp32 python optimize_pipeline.py -i ./sd_onnx_fp32 -o ./sd_onnx_fp16 --float16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 --use_io_binding ``` Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without IO Binding; IO binding gains 16ms, or 2.7%, ### Motivation and Context Optimum is working on enabling I/O binding: huggingface/optimum#2056. This could help testing the impact of I/O binding on the performance of the stable diffusion.
ankitm3k
pushed a commit
to intel/onnxruntime
that referenced
this pull request
Dec 11, 2024
…osoft#22834) ### Description Update stable diffusion benchmark: (1) allow IO binding for optimum. (2) do not use num_images_per_prompt across all engines for fair comparison. Example to run benchmark of optimum on stable diffusion 1.5: ``` git clone https://github.com/tianleiwu/optimum cd optimum git checkout tlwu/diffusers-io-binding pip install -e . pip install -U onnxruntime-gpu git clone https://github.com/microsoft/onnxruntime cd onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion git checkout tlwu/benchmark_sd_optimum_io_binding pip install -r requirements/cuda12/requirements.txt optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 --task text-to-image ./sd_onnx_fp32 python optimize_pipeline.py -i ./sd_onnx_fp32 -o ./sd_onnx_fp16 --float16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 --use_io_binding ``` Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without IO Binding; IO binding gains 16ms, or 2.7%, ### Motivation and Context Optimum is working on enabling I/O binding: huggingface/optimum#2056. This could help testing the impact of I/O binding on the performance of the stable diffusion.
ankitm3k
pushed a commit
to intel/onnxruntime
that referenced
this pull request
Dec 11, 2024
…osoft#22834) ### Description Update stable diffusion benchmark: (1) allow IO binding for optimum. (2) do not use num_images_per_prompt across all engines for fair comparison. Example to run benchmark of optimum on stable diffusion 1.5: ``` git clone https://github.com/tianleiwu/optimum cd optimum git checkout tlwu/diffusers-io-binding pip install -e . pip install -U onnxruntime-gpu git clone https://github.com/microsoft/onnxruntime cd onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion git checkout tlwu/benchmark_sd_optimum_io_binding pip install -r requirements/cuda12/requirements.txt optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 --task text-to-image ./sd_onnx_fp32 python optimize_pipeline.py -i ./sd_onnx_fp32 -o ./sd_onnx_fp16 --float16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 --use_io_binding ``` Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without IO Binding; IO binding gains 16ms, or 2.7%, ### Motivation and Context Optimum is working on enabling I/O binding: huggingface/optimum#2056. This could help testing the impact of I/O binding on the performance of the stable diffusion.
alex-spacemit
pushed a commit
to spacemit-com/onnxruntime
that referenced
this pull request
Jun 22, 2025
[ARM] MatMulNBits FP16 support - kernels only (microsoft#22806) A break down PR of microsoft#22651 Add fp16 kernels. <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Revert Implement DML copy for Lora Adapters (microsoft#22814) Revert microsoft#22396 Fix issue microsoft#22796 - a typo: (__GNUC__ > 9) -> (__GNUC__ > 10) (microsoft#22807) fix microsoft#22796 Signed-off-by: liqunfu <[email protected]> [js/webgpu] Add scatterND (microsoft#22755) <!-- Describe your changes. --> <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> [WebNN] Remove validation for coordinate_transformation_mode (microsoft#22811) The performance cost of falling back to the CPU EP is high for several resampling nodes and causes multiple partitions in SD Turbo and VAE decoder. Since the asymmetric mode with nearest to floor and integer scales is identical to half_pixel anyway, stick with the WebNN EP. [TensorRT EP] Add new provider option to exclude nodes from running on TRT (microsoft#22681) Add new provider option `trt_op_types_to_exclude`: - User can provide op type list to be excluded from running on TRT - e.g. `trt_op_types_to_exclude="MaxPool"` There is a known performance issue with the DDS ops (NonMaxSuppression, NonZero and RoiAlign) from TRT versions 10.0 to 10.7. TRT EP excludes DDS ops from running on TRT by default, user can override default value with empty string to include all ops. Keep the model metadata on the generated EP context model (microsoft#22825) Keep the model metadata on the generated EP context model [WebNN EP] Fix issues of GRU operator (microsoft#22123) This PR fixes the spelling of the key value of the GRU operator in the map in the `GetSupportedNodes` function (Gru -> GRU) and removes the data type check for the fifth input (sequence_lens) of the GRU operator. PTAL, thanks! Auto-generated baselines by 1ES Pipeline Templates (microsoft#22817) Fix Linux python CUDA package pipeline (microsoft#22803) Making ::p optional in the Linux python CUDA package pipeline Linux stage from Python-CUDA-Packaging-Pipeline has failed since merge of microsoft#22773 [WebNN] Fix MLTensorUsage is undefined issue (microsoft#22831) `MLTensorUsage` has been removed from Chromium: https://chromium-review.googlesource.com/c/chromium/src/+/6015318, but we still need to make it compatible with old Chrome versions, so just make it `undefined` for latest Chrome version. Enable ConvReplaceWithQLinear when using ACL (microsoft#22823) Enable the ConvReplaceWithQLinear graph optimization when using the ACL execution provider. Fixes an issue where quantized Conv nodes followed by ReLU don't get converted to QLinearConv, so ACL sees the weights as mutable and therefore cannot run the Conv node. Signed-off-by: Michael Tyler <[email protected]> [CUDA] stable diffusion benchmark allows IO binding for optimum (microsoft#22834) Update stable diffusion benchmark: (1) allow IO binding for optimum. (2) do not use num_images_per_prompt across all engines for fair comparison. Example to run benchmark of optimum on stable diffusion 1.5: ``` git clone https://github.com/tianleiwu/optimum cd optimum git checkout tlwu/diffusers-io-binding pip install -e . pip install -U onnxruntime-gpu git clone https://github.com/microsoft/onnxruntime cd onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion git checkout tlwu/benchmark_sd_optimum_io_binding pip install -r requirements/cuda12/requirements.txt optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 --task text-to-image ./sd_onnx_fp32 python optimize_pipeline.py -i ./sd_onnx_fp32 -o ./sd_onnx_fp16 --float16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 --use_io_binding ``` Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without IO Binding; IO binding gains 16ms, or 2.7%, Optimum is working on enabling I/O binding: huggingface/optimum#2056. This could help testing the impact of I/O binding on the performance of the stable diffusion. Fix Linux CI pipeline where ep was not provided for py-packaging-linux-test-cpu.yml (microsoft#22828) Current linux-ci-pipeline was broken due to missing parameters from `py-packaging-linux-test-cpu.yml` template Fix Linux CI pipeline Register groupnorm for opset 21 (microsoft#22830) This PR registers GroupNormalization for opset 21 <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Fix spellchecks from Optional Lint (microsoft#22802) <!-- Describe your changes. --> <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Change-Id: I561dfcdadcc6fa4cda899ef3bb181f0713fadebb
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Description
Update stable diffusion benchmark:
(1) allow IO binding for optimum.
(2) do not use num_images_per_prompt across all engines for fair comparison.
Example to run benchmark of optimum on stable diffusion 1.5:
Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without IO Binding; IO binding gains 16ms, or 2.7%,
Motivation and Context
Optimum is working on enabling I/O binding: huggingface/optimum#2056. This could help testing the impact of I/O binding on the performance of the stable diffusion.