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Move quantized embedding ops to OSS. #4606
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Summary: Move quantized embedding ops to OSS. This is the first step in supporting Graph Mode Quantization for EmbeddingBag. The next step would be to look into packing for embeddings as well as graph code rewrite to support post training dynamic quantization. Differential Revision: D21949828 fbshipit-source-id: 112ace54f570611e8634092da43a63baa7651427
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This pull request was exported from Phabricator. Differential Revision: D21949828 |
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Summary: Pull Request resolved: pytorch#40076 Pull Request resolved: pytorch/glow#4606 [PyPer][quant] Add quantized embedding operators to OSS. This is the first step in supporting Graph Mode Quantization for EmbeddingBag. At a high level, the next steps would be a) Implementation of Embedding prepack/unpack operators, b) Implementation of torch.nn.quantized.dynamic.EmbeddingBag Module, c) Implementation of torch.nn.quantized.EmbeddingBag Module, d) Implementation (modification) of IR passes to support graph quantization of EmbeddingBag module. More in-depth details regarding each step will be in the follow up diffs. Consider this as an initial diff that moves operators to respective places that's required for us to proceed. Test Plan: ```buck test mode/no-gpu caffe2/test:quantization -- --stress-runs 100 test_embedding_bag``` Reviewed By: supriyar Differential Revision: D21949828 fbshipit-source-id: 47762368b5d08fe38a5e529b58452995477a6e1b
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Summary: Move quantized embedding ops to OSS. This is the first step in supporting Graph Mode Quantization for EmbeddingBag. The next step would be to look into packing for embeddings as well as graph code rewrite to support post training dynamic quantization.
Differential Revision: D21949828