Support named_data in flat_tensor#10527
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Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/10527
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Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/) ghstack-source-id: 280709835 Pull Request resolved: #10527
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Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/) [ghstack-poisoned]
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Pull Request resolved: #10527 Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. ghstack-source-id: 280786211 Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/)
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This pull request was exported from Phabricator. Differential Revision: D73679683 |
Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/) [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D73679683 |
Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/) [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D73679683 |
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Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions. Add support to return named_data values as well. TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code. Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: #10527 by @lucylq ^ Please use this as the source of truth for the PR details, comments, and reviews ghstack PR base: https://github.com/pytorch/executorch/tree/gh/lucylq/68/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/lucylq/68/head Merge bot PR base: https://github.com/pytorch/executorch/tree/main Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/lucylq/68/orig @diff-train-skip-merge Co-authored-by: lucylq <lfq@meta.com>
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Stack from ghstack (oldest at bottom):
Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions.
Add support to return named_data values as well.
TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code.
Differential Revision: D73679683