LayerNormalization broadcast (limited support for axis=2) #23297
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kunal-vaishnavi
previously approved these changes
Jan 10, 2025
kunal-vaishnavi
approved these changes
Jan 10, 2025
11 tasks
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
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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.