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Fix model export for dense features #394
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Summary: Pull Request resolved: facebookresearch#394 DocClassificationTask supports dense features, but exporting a DocClassification model with dense features to caffe2 doesn't work. Also, exporting any model that uses both text features (word/char) and dense features together doesn't work. This diff fixes export for dense features. There are two things that needed fixing: - export puts model inputs in a list like this: `[features, feature lengths]`. However, dense features don't go through the representation layer - so they need to be hardcoded to be at the end of model inputs (ugh). The order of features needs to be `[features, feature lengths, dense features]` - dummy_model_input for all other fields has two entries. dummy_model_input for dense_features has one entry This is a blocking feature for [on-device M-suggestions](https://fb.workplace.com/groups/323789728341681/permalink/326686971385290/). A public test for that is scheduled next week. Personalization models in M-suggestions need dense features. Differential Revision: D14473020 fbshipit-source-id: 6c97c115d4140f72d0e1a97c309d95b1c8794b31
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Summary: Pull Request resolved: facebookresearch#394 DocClassificationTask supports dense features, but exporting a DocClassification model with dense features to caffe2 doesn't work. Also, exporting any model that uses both text features (word/char) and dense features together doesn't work. This diff fixes export for dense features. There are two things that needed fixing: - export puts model inputs in a list like this: `[features, feature lengths]`. However, dense features don't go through the representation layer - so they need to be hardcoded to be at the end of model inputs (ugh). The order of features needs to be `[features, feature lengths, dense features]` - dummy_model_input for all other fields has two entries. dummy_model_input for dense_features has one entry This is a blocking feature for [on-device M-suggestions](https://fb.workplace.com/groups/323789728341681/permalink/326686971385290/). A public test for that is scheduled next week. Personalization models in M-suggestions need dense features. Differential Revision: D14473020 fbshipit-source-id: afe1034f8152a7a07423902533026d83d9f57620
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…bookresearch#394) Summary: Pull Request resolved: facebookresearch#394 DocClassificationTask supports dense features, but exporting a DocClassification model with dense features to caffe2 doesn't work. Also, exporting any model that uses both text features (word/char) and dense features together doesn't work. This diff fixes export for dense features. There are two things that needed fixing: - exporter puts model inputs in a list like this: `[features, feature lengths]`. However, dense features don't go through the representation layer - so they need to be hardcoded to be at the end of model inputs (ugh). The order of features needs to be `[features, feature lengths, dense features]` - dummy_model_input for all other fields has two entries. dummy_model_input for dense_features has one entry In the long run, exporter should be tightly coupled with the model itself - we should enforce that model inputs have the same order while training and exporting. But that requires a lot of refactoring. This is a blocking feature for [on-device M-suggestions](https://fb.workplace.com/groups/323789728341681/permalink/326686971385290/). A public test for that is scheduled next week. Personalization models in M-suggestions need dense features. Reviewed By: snisarg, gardenia22 Differential Revision: D14473020 fbshipit-source-id: b5ac168b23562a2d7eed0c97cca6a1dd3667750a
…bookresearch#394) Summary: Pull Request resolved: facebookresearch#394 DocClassificationTask supports dense features, but exporting a DocClassification model with dense features to caffe2 doesn't work. Also, exporting any model that uses both text features (word/char) and dense features together doesn't work. This diff fixes export for dense features. There are two things that needed fixing: - exporter puts model inputs in a list like this: `[features, feature lengths]`. However, dense features don't go through the representation layer - so they need to be hardcoded to be at the end of model inputs (ugh). The order of features needs to be `[features, feature lengths, dense features]` - dummy_model_input for all other fields has two entries. dummy_model_input for dense_features has one entry In the long run, exporter should be tightly coupled with the model itself - we should enforce that model inputs have the same order while training and exporting. But that requires a lot of refactoring. This is a blocking feature for [on-device M-suggestions](https://fb.workplace.com/groups/323789728341681/permalink/326686971385290/). A public test for that is scheduled next week. Personalization models in M-suggestions need dense features. Reviewed By: snisarg, gardenia22 Differential Revision: D14473020 fbshipit-source-id: 8d26ff3ffd28087d6172d66f39d1d3f5f8a0ee9b
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Summary:
DocClassificationTask supports dense features, but exporting a DocClassification model with dense features to caffe2 doesn't work. Also, exporting any model that uses both text features (word/char) and dense features together doesn't work. This diff fixes export for dense features.
There are two things that needed fixing:
[features, feature lengths]. However, dense features don't go through the representation layer - so they need to be hardcoded to be at the end of model inputs (ugh). The order of features needs to be[features, feature lengths, dense features]This is a blocking feature for on-device M-suggestions. A public test for that is scheduled next week.
Personalization models in M-suggestions need dense features.
Differential Revision: D14473020