torchrec support on kvzch emb lookup module #2922
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Summary: X-link: pytorch/FBGEMM#4035 Pull Request resolved: meta-pytorch#2922 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv, emlin Differential Revision: D73567631
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Summary: X-link: pytorch/FBGEMM#4035 Pull Request resolved: meta-pytorch#2922 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv, emlin Differential Revision: D73567631
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Summary: X-link: pytorch/FBGEMM#4035 Pull Request resolved: meta-pytorch#2922 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv, emlin Differential Revision: D73567631
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Summary: X-link: pytorch/FBGEMM#4035 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv, emlin Differential Revision: D73567631
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Summary: X-link: pytorch/FBGEMM#4035 Pull Request resolved: meta-pytorch#2922 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv, emlin Differential Revision: D73567631
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Summary: X-link: pytorch/FBGEMM#4035 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv Differential Revision: D73567631
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Summary: X-link: pytorch/FBGEMM#4035 Pull Request resolved: meta-pytorch#2922 X-link: facebookresearch/FBGEMM#1120 # Change logs 1. add ZeroCollisionKeyValueEmbedding emb lookup 2. address existing unit test missing for ssd offloading 3. add new ut for kv zch embedding module 4. add a temp hack solution for calculate bucket metadata 5. embedding updates, details illustrated below ####################################################################### ########################### embedding.py updates ########################## ####################################################################### 1. keep the original idea to init shardedTensor during training init 2. for kv zch table, the shardeTensor will be init using virtual size for metadata calculation, and skip actual tensor size check for ST init, this is needed as during training init, the table has 0 rows 3. the new tensor, weight_id will not be registered in the EC becuase its shape is changing in realtime, the weight_id tensor will be generated in post_state_dict hooks 4. the new tensor, bucket could be registered and preserved, but in this diff we keep it the same way as weight_id 5. in post state dict hook, we call get_named_split_embedding_weights_snapshot to get Tuple[table_name, weight(ST), weight_id(ST), bucket(ST)], all 3 tensors are return in the format of ST, and we will update destination with the returned ST directly 6. in pre_load_state_dict_hook, which is called upon load_state_dict(), we will skip all 3 tensors update, because the tensor assignment is done [on the nn.module side](https://fburl.com/code/it5nior8), which doesn't support updating KVT through PMT. This is fine for now because, checkpoint loading will be done outside of the load_state_dict call, but we need future plans to make it work cohesively with other type of tensors Reviewed By: kausv Differential Revision: D73567631
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Change logs
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########################### embedding.py updates ##########################
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Differential Revision: D73567631