LayerNormalization broadcast (limited support for axis=2) #23297
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kunal-vaishnavi
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Jan 10, 2025
kunal-vaishnavi
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Jan 10, 2025
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guschmue
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Jan 12, 2025
### Description Spec of LayerNormalization supports broadcasting (tensors Scale and B should be unidirectional broadcastable to tensor X). https://onnx.ai/onnx/operators/onnx__LayerNormalization.html However, current implementation only allow scale and bias size to be X.shape()[axis:]. Example of input tensors that normalized with axis=2: | X shape | Scale shape | B shape | Before | After | | - | - | - | - | - | | (B, S, D) | (D) | (D) | Supported | Supported | | (B, S, D) | (1, 1, D) | (1, 1, D) | Supported | Supported | | (B, S, D) | (B, 1, D) | (B, 1, D) | Not Supported | Supported | | (B, S, D) | (1, S, D) | (1, S, D) | Not Supported | Supported | | (B, S, D) | (B, S, D) | (B, S, D) | Not Supported | Supported | Here we add limited support: axis=2; scale/bias has same shape; scale/bias/X have same number of dimensions. It could support common use case in LLM and vision models. ### Motivation and Context Support Stable Diffusion 3.x and Flux model.
tianleiwu
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Jan 14, 2025
### Description It has dependency on the following PRs: - #23297 Optimize the ONNX pipeline for Stable Diffusion 3.x and Flux 1.0 models (fp32 or fp16). - [x] Update optimize_pipeline script - [x] Update benchmkark script - [x] Update document about Stable Diffusion 3.x and Flux 1.0 models - [x] Add graph optimizations for MMDit model - [x] FastGelu fusion - [x] RMSNorm fusion - [x] MultiHeadAttention fusion - [x] Add graph optimizations for Flux transformer models - [x] MultiHeadAttention fusion - [x] Update graph optimizations for t5 - [x] Add tests Optimize the ONNX pipeline for Stable Diffusion 3.x and Flux 1.0 models: ``` python optimize_pipeline.py -i ./flux1_schnell_onnx/fp32 -o ./flux1_schnell_onnx/fp16 --float16 Optimize flux1_schnell_onnx/fp32/transformer/model.onnx ... Fused LayerNormalization: 115 Fused SimplifiedLayerNormalization: 152 Fused FastGelu: 76 Fused MultiHeadAttention: 57 ``` ### H100 Benchmark Results * GPU: NVIDIA H100 80GB HBM3 * Image Size: 1024x1024 * Batch Size: 1 Model | Steps | Precision | Engine | Latency (Seconds) | GPU Memory (MB) -- | -- | -- | -- | -- | -- Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (compile) | 8.198 | 37,603 Flux 1.0 Dev | 50 | FP16+BF16 | Optimum (ORT) | 10.762 | 41,469 Flux 1.0 Dev | 50 | FP16+FP32 | Optimum (ORT) | 10.891 | 43,545 Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (eager) | 12.339 | 36,651 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (compile) | 0.775 | 37,857 Flux 1.0 Schnell | 4 | FP16+BF16 | Optimum (ORT) | 0.931 | 41,433 Flux 1.0 Schnell | 4 | FP16+FP32 | Optimum (ORT) | 0.939 | 43,809 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (eager) | 1.120 | 36,629 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (compile) | 7.466 | 32,217 SD 3.5 Large | 50 | FP16+BF16 | Optimum (ORT) | 10.275 | 36,609 SD 3.5 Large | 50 | FP16+FP32 | Optimum (ORT) | 10.283 | 36,729 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (eager) | 11.615 | 31,517 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (compile) | 3.240 | 21,143 SD 3.5 Medium | 50 | FP16+BF16 | Optimum (ORT) | 4.799 | 25,097 SD 3.5 Medium | 50 | FP16+FP32 | Optimum (ORT) | 4.838 | 25,109 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (eager) | 5.582 | 20,489 ### A100 Benchmark Results * GPU: A100-SXM4-80GB * Image Size: 1024x1024 * Batch Size: 1 Model | Steps | Precision | Engine | Latency (Seconds) | GPU Memory (MB) -- | -- | -- | -- | -- | -- Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (compile) | 17.593 | 37,723 Flux 1.0 Dev | 50 | FP16+BF16 | Optimum (ORT) | 21.918 | 41,348 Flux 1.0 Dev | 50 | FP16+FP32 | Optimum (ORT) | 22.060 | 44,860 Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (eager) | 24.267 | 36,847 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (compile) | 1.627 | 37,881 Flux 1.0 Schnell | 4 | FP16+BF16 | Optimum (ORT) | 1.884 | 41,537 Flux 1.0 Schnell | 4 | FP16+FP32 | Optimum (ORT) | 1.902 | 44,858 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (eager) | 2.162 | 36,831 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (compile) | 15.881 | 32,307 SD 3.5 Large | 50 | FP16+FP32 | Optimum (ORT) | 19.837 | 36,451 SD 3.5 Large | 50 | FP16+BF16 | Optimum (ORT) | 19.964 | 36,461 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (eager) | 22.477 | 31,513 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (compile) | 6.476 | 21,341 SD 3.5 Medium | 50 | FP16+FP32 | Optimum (ORT) | 8.775 | 25,183 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (eager) | 10.057 | 20,433 ### Future Works * Triton kernel for matrix multiplication and auto tuning. * FP8/Int8 quantization ### Motivation and Context SD 3.5 Architecture: https://huggingface.co/stabilityai/stable-diffusion-3.5-medium/resolve/main/mmdit-x.png
guschmue
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Mar 6, 2025
### Description It has dependency on the following PRs: - #23297 Optimize the ONNX pipeline for Stable Diffusion 3.x and Flux 1.0 models (fp32 or fp16). - [x] Update optimize_pipeline script - [x] Update benchmkark script - [x] Update document about Stable Diffusion 3.x and Flux 1.0 models - [x] Add graph optimizations for MMDit model - [x] FastGelu fusion - [x] RMSNorm fusion - [x] MultiHeadAttention fusion - [x] Add graph optimizations for Flux transformer models - [x] MultiHeadAttention fusion - [x] Update graph optimizations for t5 - [x] Add tests Optimize the ONNX pipeline for Stable Diffusion 3.x and Flux 1.0 models: ``` python optimize_pipeline.py -i ./flux1_schnell_onnx/fp32 -o ./flux1_schnell_onnx/fp16 --float16 Optimize flux1_schnell_onnx/fp32/transformer/model.onnx ... Fused LayerNormalization: 115 Fused SimplifiedLayerNormalization: 152 Fused FastGelu: 76 Fused MultiHeadAttention: 57 ``` ### H100 Benchmark Results * GPU: NVIDIA H100 80GB HBM3 * Image Size: 1024x1024 * Batch Size: 1 Model | Steps | Precision | Engine | Latency (Seconds) | GPU Memory (MB) -- | -- | -- | -- | -- | -- Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (compile) | 8.198 | 37,603 Flux 1.0 Dev | 50 | FP16+BF16 | Optimum (ORT) | 10.762 | 41,469 Flux 1.0 Dev | 50 | FP16+FP32 | Optimum (ORT) | 10.891 | 43,545 Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (eager) | 12.339 | 36,651 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (compile) | 0.775 | 37,857 Flux 1.0 Schnell | 4 | FP16+BF16 | Optimum (ORT) | 0.931 | 41,433 Flux 1.0 Schnell | 4 | FP16+FP32 | Optimum (ORT) | 0.939 | 43,809 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (eager) | 1.120 | 36,629 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (compile) | 7.466 | 32,217 SD 3.5 Large | 50 | FP16+BF16 | Optimum (ORT) | 10.275 | 36,609 SD 3.5 Large | 50 | FP16+FP32 | Optimum (ORT) | 10.283 | 36,729 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (eager) | 11.615 | 31,517 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (compile) | 3.240 | 21,143 SD 3.5 Medium | 50 | FP16+BF16 | Optimum (ORT) | 4.799 | 25,097 SD 3.5 Medium | 50 | FP16+FP32 | Optimum (ORT) | 4.838 | 25,109 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (eager) | 5.582 | 20,489 ### A100 Benchmark Results * GPU: A100-SXM4-80GB * Image Size: 1024x1024 * Batch Size: 1 Model | Steps | Precision | Engine | Latency (Seconds) | GPU Memory (MB) -- | -- | -- | -- | -- | -- Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (compile) | 17.593 | 37,723 Flux 1.0 Dev | 50 | FP16+BF16 | Optimum (ORT) | 21.918 | 41,348 Flux 1.0 Dev | 50 | FP16+FP32 | Optimum (ORT) | 22.060 | 44,860 Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (eager) | 24.267 | 36,847 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (compile) | 1.627 | 37,881 Flux 1.0 Schnell | 4 | FP16+BF16 | Optimum (ORT) | 1.884 | 41,537 Flux 1.0 Schnell | 4 | FP16+FP32 | Optimum (ORT) | 1.902 | 44,858 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (eager) | 2.162 | 36,831 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (compile) | 15.881 | 32,307 SD 3.5 Large | 50 | FP16+FP32 | Optimum (ORT) | 19.837 | 36,451 SD 3.5 Large | 50 | FP16+BF16 | Optimum (ORT) | 19.964 | 36,461 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (eager) | 22.477 | 31,513 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (compile) | 6.476 | 21,341 SD 3.5 Medium | 50 | FP16+FP32 | Optimum (ORT) | 8.775 | 25,183 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (eager) | 10.057 | 20,433 ### Future Works * Triton kernel for matrix multiplication and auto tuning. * FP8/Int8 quantization ### Motivation and Context SD 3.5 Architecture: https://huggingface.co/stabilityai/stable-diffusion-3.5-medium/resolve/main/mmdit-x.png
ashrit-ms
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Mar 17, 2025
### Description Spec of LayerNormalization supports broadcasting (tensors Scale and B should be unidirectional broadcastable to tensor X). https://onnx.ai/onnx/operators/onnx__LayerNormalization.html However, current implementation only allow scale and bias size to be X.shape()[axis:]. Example of input tensors that normalized with axis=2: | X shape | Scale shape | B shape | Before | After | | - | - | - | - | - | | (B, S, D) | (D) | (D) | Supported | Supported | | (B, S, D) | (1, 1, D) | (1, 1, D) | Supported | Supported | | (B, S, D) | (B, 1, D) | (B, 1, D) | Not Supported | Supported | | (B, S, D) | (1, S, D) | (1, S, D) | Not Supported | Supported | | (B, S, D) | (B, S, D) | (B, S, D) | Not Supported | Supported | Here we add limited support: axis=2; scale/bias has same shape; scale/bias/X have same number of dimensions. It could support common use case in LLM and vision models. ### Motivation and Context Support Stable Diffusion 3.x and Flux model.
ashrit-ms
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that referenced
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Mar 17, 2025
### Description It has dependency on the following PRs: - #23297 Optimize the ONNX pipeline for Stable Diffusion 3.x and Flux 1.0 models (fp32 or fp16). - [x] Update optimize_pipeline script - [x] Update benchmkark script - [x] Update document about Stable Diffusion 3.x and Flux 1.0 models - [x] Add graph optimizations for MMDit model - [x] FastGelu fusion - [x] RMSNorm fusion - [x] MultiHeadAttention fusion - [x] Add graph optimizations for Flux transformer models - [x] MultiHeadAttention fusion - [x] Update graph optimizations for t5 - [x] Add tests Optimize the ONNX pipeline for Stable Diffusion 3.x and Flux 1.0 models: ``` python optimize_pipeline.py -i ./flux1_schnell_onnx/fp32 -o ./flux1_schnell_onnx/fp16 --float16 Optimize flux1_schnell_onnx/fp32/transformer/model.onnx ... Fused LayerNormalization: 115 Fused SimplifiedLayerNormalization: 152 Fused FastGelu: 76 Fused MultiHeadAttention: 57 ``` ### H100 Benchmark Results * GPU: NVIDIA H100 80GB HBM3 * Image Size: 1024x1024 * Batch Size: 1 Model | Steps | Precision | Engine | Latency (Seconds) | GPU Memory (MB) -- | -- | -- | -- | -- | -- Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (compile) | 8.198 | 37,603 Flux 1.0 Dev | 50 | FP16+BF16 | Optimum (ORT) | 10.762 | 41,469 Flux 1.0 Dev | 50 | FP16+FP32 | Optimum (ORT) | 10.891 | 43,545 Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (eager) | 12.339 | 36,651 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (compile) | 0.775 | 37,857 Flux 1.0 Schnell | 4 | FP16+BF16 | Optimum (ORT) | 0.931 | 41,433 Flux 1.0 Schnell | 4 | FP16+FP32 | Optimum (ORT) | 0.939 | 43,809 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (eager) | 1.120 | 36,629 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (compile) | 7.466 | 32,217 SD 3.5 Large | 50 | FP16+BF16 | Optimum (ORT) | 10.275 | 36,609 SD 3.5 Large | 50 | FP16+FP32 | Optimum (ORT) | 10.283 | 36,729 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (eager) | 11.615 | 31,517 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (compile) | 3.240 | 21,143 SD 3.5 Medium | 50 | FP16+BF16 | Optimum (ORT) | 4.799 | 25,097 SD 3.5 Medium | 50 | FP16+FP32 | Optimum (ORT) | 4.838 | 25,109 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (eager) | 5.582 | 20,489 ### A100 Benchmark Results * GPU: A100-SXM4-80GB * Image Size: 1024x1024 * Batch Size: 1 Model | Steps | Precision | Engine | Latency (Seconds) | GPU Memory (MB) -- | -- | -- | -- | -- | -- Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (compile) | 17.593 | 37,723 Flux 1.0 Dev | 50 | FP16+BF16 | Optimum (ORT) | 21.918 | 41,348 Flux 1.0 Dev | 50 | FP16+FP32 | Optimum (ORT) | 22.060 | 44,860 Flux 1.0 Dev | 50 | BF16 | Torch 2.5.1 (eager) | 24.267 | 36,847 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (compile) | 1.627 | 37,881 Flux 1.0 Schnell | 4 | FP16+BF16 | Optimum (ORT) | 1.884 | 41,537 Flux 1.0 Schnell | 4 | FP16+FP32 | Optimum (ORT) | 1.902 | 44,858 Flux 1.0 Schnell | 4 | BF16 | Torch 2.5.1 (eager) | 2.162 | 36,831 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (compile) | 15.881 | 32,307 SD 3.5 Large | 50 | FP16+FP32 | Optimum (ORT) | 19.837 | 36,451 SD 3.5 Large | 50 | FP16+BF16 | Optimum (ORT) | 19.964 | 36,461 SD 3.5 Large | 50 | BF16 | Torch 2.5.1 (eager) | 22.477 | 31,513 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (compile) | 6.476 | 21,341 SD 3.5 Medium | 50 | FP16+FP32 | Optimum (ORT) | 8.775 | 25,183 SD 3.5 Medium | 50 | BF16 | Torch 2.5.1 (eager) | 10.057 | 20,433 ### Future Works * Triton kernel for matrix multiplication and auto tuning. * FP8/Int8 quantization ### Motivation and Context SD 3.5 Architecture: https://huggingface.co/stabilityai/stable-diffusion-3.5-medium/resolve/main/mmdit-x.png
tianleiwu
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Dec 11, 2025
…ion (#26613) ### Description <!-- Describe your changes. --> This PR adds full and spec-compliant broadcasting support to both LayerNormalization and RMSNormalization. Previously, onnxruntime supported only a partial set of broadcasting cases (based on the logic introduced in this PR #23297 ). That implementation handled several cases but did not cover all valid broadcasting scenarios. This PR introduces a complete generic broadcasting path, following the [ONNX specification rules](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md). The previous implementation is preserved as a fast-path and is still used whenever the Scale/Bias shapes match directly. Main changes: - Extended broadcasting logic in: layer_norm_helper.h layer_norm_impl.cc - Added full support for all valid broadcasting configurations of Scale and Bias. - Preserved previous partial logic as a fast-path for exact-match cases. - Added comprehensive tests to: layer_norm_op_test.cc rms_norm_op_test.cc ### Motivation and Context <!-- - Why is this change required? What problem does it solve?--> Before this fix, some valid ONNX broadcasting shapes were rejected in LayerNormalization and RMSNormalization. This PR brings the operators into full alignment with the ONNX specification and fixes models that previously failed due to incomplete broadcasting support. Fixes #26432 Fixes #18184 <!-- -If it fixes an open issue, please link to the issue here. -->
Sumit2318
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Jan 6, 2026
…ion (#26613) ### Description <!-- Describe your changes. --> This PR adds full and spec-compliant broadcasting support to both LayerNormalization and RMSNormalization. Previously, onnxruntime supported only a partial set of broadcasting cases (based on the logic introduced in this PR #23297 ). That implementation handled several cases but did not cover all valid broadcasting scenarios. This PR introduces a complete generic broadcasting path, following the [ONNX specification rules](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md). The previous implementation is preserved as a fast-path and is still used whenever the Scale/Bias shapes match directly. Main changes: - Extended broadcasting logic in: layer_norm_helper.h layer_norm_impl.cc - Added full support for all valid broadcasting configurations of Scale and Bias. - Preserved previous partial logic as a fast-path for exact-match cases. - Added comprehensive tests to: layer_norm_op_test.cc rms_norm_op_test.cc ### Motivation and Context <!-- - Why is this change required? What problem does it solve?--> Before this fix, some valid ONNX broadcasting shapes were rejected in LayerNormalization and RMSNormalization. This PR brings the operators into full alignment with the ONNX specification and fixes models that previously failed due to incomplete broadcasting support. Fixes #26432 Fixes #18184 <!-- -If it fixes an open issue, please link to the issue here. -->
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Description
Spec of LayerNormalization supports broadcasting (tensors Scale and B should be unidirectional broadcastable to tensor X).
https://onnx.ai/onnx/operators/onnx__LayerNormalization.html
However, current implementation only allow scale and bias size to be X.shape()[axis:].
Example of input tensors that normalized with axis=2:
Here we add limited support: axis=2; scale/bias has same shape; scale/bias/X have same number of dimensions. It could support common use case in LLM and vision models.
Motivation and Context
Support Stable Diffusion 3.x and Flux model.