fuse conv and batch_norm #3769
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fuse conv and batch_norm #3769
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/3769
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit f84a2f5 with merge base 56a6855 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Summary: When `batchnorm` is applied after `conv` in a model, we can fuse the weight and bias of `batchnorm` into `conv` and thereafter remove the `batchnorm` node. We implement this fusion through graph transforms and apply it in `vulkan_preprocess.py`. This change can reduce both the latency and memory. We illustrate the performance improvement with Mobilenet_v2. - The model has 52 conv+batch_norm instances. After fusing, when we export the model as in D57475757, `_native_batch_norm_legit_no_training` doesn't show up anymore. - The performance has been improved as below. In particular, inference latency has been reduced from 161 ms to 148 ms. | fuse | Loading(ms) | vmRss(KB) | vmaBlock(KB) | Inference(ms) | vmRss(KB) | vmaBlock(KB) | | -------- | ------- | ------- | ------- | ------- | | Yes | 380 | 22928 | 65536 | 148 | 24296 | 65536 | | No | 473 | 26036 | 65536 | 161 | 27416 | 65536 | Differential Revision: D57895439
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This pull request has been merged in 4f2f7e0. |
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Summary: We implemented [operators fusion](pytorch#3769) (`conv+bn`) which fused `conv` and `bn`'s weights and biases, but the old parameters are not deleted. Hence we saw that VK model's size is nearly twice large as CPU's. As regards mobilenet_v2, before this diff CPU vs VK: 14M vs 22M. After this diff, both of them have 14M. Differential Revision: D60257047
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Summary: Pull Request resolved: pytorch#4427 We implemented [operators fusion](pytorch#3769) (`conv+bn`) which fused `conv` and `bn`'s weights and biases, but the old parameters are not deleted. Hence we saw that VK model's size is nearly twice large as CPU's. As regards mobilenet_v2, before this diff CPU vs VK: 14M vs 22M. After this diff, both of them have 14M. Reviewed By: SS-JIA Differential Revision: D60257047
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Summary: Pull Request resolved: pytorch#4427 We implemented [operators fusion](pytorch#3769) (`conv+bn`) which fused `conv` and `bn`'s weights and biases, but the old parameters are not deleted. Hence we saw that VK model's size is nearly twice large as CPU's. As regards mobilenet_v2, before this diff CPU vs VK: 14M vs 22M. After this diff, both of them have 14M. Reviewed By: SS-JIA Differential Revision: D60257047
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Summary: Pull Request resolved: pytorch#4427 We implemented [operators fusion](pytorch#3769) (`conv+bn`) which fused `conv` and `bn`'s weights and biases, but the old parameters are not deleted. Hence we saw that VK model's size is nearly twice large as CPU's. As regards mobilenet_v2, before this diff CPU vs VK: 14M vs 22M. After this diff, both of them have 14M. Reviewed By: SS-JIA Differential Revision: D60257047
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Summary: Pull Request resolved: pytorch#4427 We implemented [operators fusion](pytorch#3769) (`conv+bn`) which fused `conv` and `bn`'s weights and biases, but the old parameters are not deleted. Hence we saw that VK model's size is nearly twice large as CPU's. As regards mobilenet_v2, before this diff CPU vs VK: 14M vs 22M. After this diff, both of them have 14M. Reviewed By: SS-JIA Differential Revision: D60257047
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Summary: Pull Request resolved: #4427 We implemented [operators fusion](#3769) (`conv+bn`) which fused `conv` and `bn`'s weights and biases, but the old parameters are not deleted. Hence we saw that VK model's size is nearly twice large as CPU's. As regards mobilenet_v2, before this diff CPU vs VK: 14M vs 22M. After this diff, both of them have 14M. Reviewed By: SS-JIA Differential Revision: D60257047 fbshipit-source-id: ca9e0f38d53187edff9dba45fdeffa619fde51a7
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Summary:
batch_normanymore| fuse | Loading(ms) | vmRss(KB) | vmaBlock(KB) | Inference(ms) | vmRss(KB) | vmaBlock(KB) |
| -------- | ------- | ------- | ------- | ------- |
| Yes | 380 | 22928 | 65536 | 148 | 24296 | 65536 |
| No | 473 | 26036 | 65536 | 161 | 27416 | 65536 |
Differential Revision: D57895439