Adding RETRO model Faiss sharding index and KNN sharding index #4713

yidong72 · 2022-08-09T22:02:00Z

What does this PR do ?

To handle very large dataset, e.g. hundreds of gigabyte to terrabyte compressed raw data, we need multiple nodes to create sharding index and combine them together.
It enhance the KNN index data structure to handling sharding index.

In this PR, it creates GPU Faiss index in 3 stages.

stage 0, train Faiss index structure.
stage 1, create Faiss sharding index
stage 2, merge sharding index into one.

It creates KNN index in 2 stages.

stage 1, create KNN sharding index
stage 2, merge KNN sharding index into one.

It includes some unit tests to cover the KNN sharding index, dedup funcitons etc.

I have been used it to successfuly create Faiss and KNN index for 350G dataset in a slurm cluster enviroment.

Signed-off-by: Yi Dong <yidong@nvidia.com>

lgtm-com · 2022-08-19T19:13:24Z

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Signed-off-by: Yi Dong <doyend@gmail.com>

lgtm-com · 2022-08-23T03:27:22Z

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Signed-off-by: Yi Dong <doyend@gmail.com>

lgtm-com · 2022-08-23T03:43:34Z

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Signed-off-by: Yi Dong <doyend@gmail.com>

lgtm-com · 2022-08-23T13:48:34Z

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lgtm-com · 2022-08-31T17:29:22Z

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Signed-off-by: Yi Dong <yidong@nvidia.com>

lgtm-com · 2022-08-31T17:47:07Z

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lgtm-com · 2022-08-31T18:21:13Z

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Signed-off-by: Yi Dong <yidong@nvidia.com>

lgtm-com · 2022-09-03T14:33:47Z

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michalivne

LGTM! Great PR. See minor comments regarding missing docstrings/comments

michalivne · 2022-09-06T19:11:14Z

nemo/collections/nlp/models/language_modeling/megatron_retrieval_model.py

-        # self.grad_clip_pl_default = True
+
+        if hasattr(self.cfg, "shape_file"):
+            # self.grad_clip_pl_default = True


Is this comment required?

no. removed it

michalivne · 2022-09-06T19:14:12Z

nemo/collections/nlp/models/language_modeling/megatron_retrieval_model.py

+            # add shape file
+            self.register_artifact("model.shape_file", self.cfg.shape_file),
+
+            for name, tensor in self.named_parameters():


Can you add a comment explaining this initialization scheme?

michalivne · 2022-09-06T19:17:05Z

nemo/collections/nlp/modules/common/megatron/transformer.py

-            attention_mask = inputs[3]
-            rotary_pos_emb = inputs[4]
-            relative_position_bias = inputs[5]
+            if len(inputs) == 7:


This looks very brittle.Perhaps we can use here the same solution we used in nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py with self._get_forward_output_only_func ?

I checked the self._get_forward_output_only_func method, which has a help function to convert dictionary arguments to positional arguments. Here I have to translate positional arguments into dictionary arguments. Not sure what's a better solution.

michalivne · 2022-09-06T19:24:19Z

scripts/nlp_language_modeling/build_knn_map_index.py

+    return start, total_chunks
+
+
+def process_sentence_chunks(


Can you add a docsring describing what the function?

michalivne · 2022-09-06T19:29:16Z

scripts/nlp_language_modeling/build_knn_map_index.py

+        logging.info(f'Index neighbors: {f.K}')
+        logging.info(f'Index chunk start id: {f.chunk_start_id}')
+        logging.info(f'Index chunk end id: {f.chunk_end_id}')
+        sys.exit(0)


Can you add a few more high level comments in the code below explaining the purpose of code blocks?

I do have a high level comment about the script at the beginning of the file with some examples of how to use it.

michalivne · 2022-09-06T19:31:45Z

scripts/nlp_language_modeling/exam_knn_map_quality.py

@@ -105,7 +105,7 @@ def get_tokenizer(args):
    logging.info(f'Data index has {data_ds.chunks} chunks')
    logging.info(f'Retrieval Data index has {retrieval_ds.chunks} chunks')
    logging.info(f'KNN index has {knn_index.K} neighbors')
-    assert knn_index.knn_map.max() < retrieval_ds.chunks
+    # assert knn_index.knn_map.max() < retrieval_ds.chunks


Remove comment if not needed

michalivne · 2022-09-06T19:33:49Z

scripts/nlp_language_modeling/build_retrieval_index.py

@@ -68,7 +125,15 @@ def get_tokenizer(args):


 def process_sentence_chunks(
-    ds: MMapRetrievalIndexedDataset, tokenizer, chunk_size: int, warm_up_size: int, percent: float
+    ds: MMapRetrievalIndexedDataset,


Can you add a docstring?

soumye · 2022-09-06T22:01:13Z

scripts/nlp_language_modeling/build_knn_map_index.py

+            position += 1
+
+
+def dedup(chunk_id_to_range, I, tmp_neighbors, chunk_id_start, offset):
    """
    deduplicate the KNN who are from the same document as the data chunks.


Why do we want to do this? Can we not have multiple neighbors from a given document? Or am I misunderstanding something here?

This is RETRO's trick to train on the same data that is used as retrieval data. We simply remove the neighbors from the same document as the input data chunk.

soumye · 2022-09-06T22:01:17Z

scripts/nlp_language_modeling/build_knn_map_index.py

+            position += 1
+
+
+def dedup(chunk_id_to_range, I, tmp_neighbors, chunk_id_start, offset):
    """
    deduplicate the KNN who are from the same document as the data chunks.


Why do we want to do this? Can we not have multiple neighbors from a given document? Or am I misunderstanding something here?

soumye · 2022-09-06T22:11:44Z

scripts/nlp_language_modeling/build_knn_map_index.py

@@ -175,6 +264,8 @@ def get_emb():
        default='bert-base-nli-mean-tokens',


@yidong72 I thought you were now using all-mpnet-base-v2

yes. this is just the default argument. When using this script, I set it to all-mpnet-base-v2

sounds good

soumye · 2022-09-06T22:16:56Z

scripts/nlp_language_modeling/build_retrieval_index.py

+stage-0: train on the dataset, example,
+
+```python
+python scripts/nlp_language_modeling/build_retrieval_index.py \


Training uses the entirety of the dataset right?

For training the Faiss index, we use a fraction of the chunks specified by train_index_size

soumye

The PR looks great to me. I have added some comments.

soumye · 2022-09-06T22:19:15Z

scripts/nlp_language_modeling/build_retrieval_index.py

    )
    parser.add_argument(
-        '--train_index_size', type=int, required=True, help='The number of sentences that is used to train the index',
+        '--train_index_size', type=int, required=False, help='The number of sentences that is used to train the index',


So do you use the entirety or only a subset of data to train the index?

use a subset of the data. It is impractical to use all the data as my dataset can be as large as 200billion tokens.

I see. Makes sense. I am guessing it shouldn't even matter that much for performance.

scripts/nlp_language_modeling/build_retrieval_index.py

Signed-off-by: Yi Dong <doyend@gmail.com>

lgtm-com · 2022-09-07T01:12:50Z

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michalivne

LGTM!

…A#4713) * dump shape Signed-off-by: Yi Dong <yidong@nvidia.com> * fix the readout layer Signed-off-by: Yi Dong <yidong@nvidia.com> * working Signed-off-by: Yi Dong <yidong@nvidia.com> * remove the scaling Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * save file by config Signed-off-by: Yi Dong <yidong@nvidia.com> * confirm tp works Signed-off-by: Yi Dong <yidong@nvidia.com> * added support of ckpt conversion Signed-off-by: Yi Dong <yidong@nvidia.com> * add loss scale Signed-off-by: Yi Dong <yidong@nvidia.com> * padding only Signed-off-by: Yi Dong <yidong@nvidia.com> * add comments, set normfactor in file Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * set it norm factor correctly Signed-off-by: Yi Dong <yidong@nvidia.com> * match the original one Signed-off-by: Yi Dong <yidong@nvidia.com> * fix weight init Signed-off-by: Yi Dong <yidong@nvidia.com> * hard code the output std Signed-off-by: Yi Dong <yidong@nvidia.com> * seq parallel base Signed-off-by: Yi Dong <yidong@nvidia.com> * recursively process the files Signed-off-by: Yi Dong <yidong@nvidia.com> * add filter filter Signed-off-by: Yi Dong <yidong@nvidia.com> * add the shape file Signed-off-by: Yi Dong <yidong@nvidia.com> * fix the inconsistence in training Signed-off-by: Yi Dong <yidong@nvidia.com> * register optimizers Signed-off-by: Yi Dong <yidong@nvidia.com> * fix the style Signed-off-by: Yi Dong <yidong@nvidia.com> * fix LGTM Signed-off-by: Yi Dong <yidong@nvidia.com> * added model training performance ci test Signed-off-by: Yi Dong <yidong@nvidia.com> * escape properly Signed-off-by: Yi Dong <yidong@nvidia.com> * multiple line quote Signed-off-by: Yi Dong <yidong@nvidia.com> * added python to ci test Signed-off-by: Yi Dong <yidong@nvidia.com> * reduce accuracy check Signed-off-by: Yi Dong <yidong@nvidia.com> * use the same data mapping Signed-off-by: Yi Dong <yidong@nvidia.com> * only test it on A100 gpu Signed-off-by: Yi Dong <yidong@nvidia.com> * added license and use base yaml conf Signed-off-by: Yi Dong <yidong@nvidia.com> * stage 0 Signed-off-by: Yi Dong <yidong@nvidia.com> * add time info Signed-off-by: Yi Dong <yidong@nvidia.com> * fix one bug Signed-off-by: Yi Dong <yidong@nvidia.com> * reduce nlist Signed-off-by: Yi Dong <yidong@nvidia.com> * stage 1 Signed-off-by: Yi Dong <yidong@nvidia.com> * working stage 1 Signed-off-by: Yi Dong <yidong@nvidia.com> * stage 2 Signed-off-by: Yi Dong <yidong@nvidia.com> * fix one bug Signed-off-by: Yi Dong <yidong@nvidia.com> * time add index Signed-off-by: Yi Dong <yidong@nvidia.com> * document data loading time Signed-off-by: Yi Dong <yidong@nvidia.com> * works with CPU only environment Signed-off-by: Yi Dong <yidong@nvidia.com> * add doc string Signed-off-by: Yi Dong <yidong@nvidia.com> * handle gpu faiss Signed-off-by: Yi Dong <yidong@nvidia.com> * added sharding knn index Signed-off-by: Yi Dong <yidong@nvidia.com> * added unit test for sharding index Signed-off-by: Yi Dong <yidong@nvidia.com> * knn stage processing Signed-off-by: Yi Dong <yidong@nvidia.com> * fix shard gpu index Signed-off-by: Yi Dong <yidong@nvidia.com> * add the unit test for dedup logics Signed-off-by: Yi Dong <yidong@nvidia.com> * fix parallel jit Signed-off-by: Yi Dong <yidong@nvidia.com> * added doc Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * use the sharded version Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * fix save checkpoint Signed-off-by: Yi Dong <yidong@nvidia.com> * fix id update Signed-off-by: Yi Dong <yidong@nvidia.com> * move to constructor; Signed-off-by: Yi Dong <doyend@gmail.com> * fix the test Signed-off-by: Yi Dong <doyend@gmail.com> * make checkpoint activation work Signed-off-by: Yi Dong <doyend@gmail.com> * reduce duplicate Signed-off-by: Yi Dong <doyend@gmail.com> * fix selective attention Signed-off-by: Yi Dong <doyend@gmail.com> * fix the style Signed-off-by: Yi Dong <yidong@nvidia.com> * remove the max check Signed-off-by: Yi Dong <yidong@nvidia.com> * added comments Signed-off-by: Yi Dong <doyend@gmail.com> Signed-off-by: Yi Dong <yidong@nvidia.com> Signed-off-by: Yi Dong <doyend@gmail.com> Co-authored-by: Yi Dong <doyend@gmail.com> Co-authored-by: Oleksii Kuchaiev <okuchaiev@users.noreply.github.com> Signed-off-by: Matvei Novikov <mattyson.so@gmail.com>

…A#4713) * dump shape Signed-off-by: Yi Dong <yidong@nvidia.com> * fix the readout layer Signed-off-by: Yi Dong <yidong@nvidia.com> * working Signed-off-by: Yi Dong <yidong@nvidia.com> * remove the scaling Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * save file by config Signed-off-by: Yi Dong <yidong@nvidia.com> * confirm tp works Signed-off-by: Yi Dong <yidong@nvidia.com> * added support of ckpt conversion Signed-off-by: Yi Dong <yidong@nvidia.com> * add loss scale Signed-off-by: Yi Dong <yidong@nvidia.com> * padding only Signed-off-by: Yi Dong <yidong@nvidia.com> * add comments, set normfactor in file Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * set it norm factor correctly Signed-off-by: Yi Dong <yidong@nvidia.com> * match the original one Signed-off-by: Yi Dong <yidong@nvidia.com> * fix weight init Signed-off-by: Yi Dong <yidong@nvidia.com> * hard code the output std Signed-off-by: Yi Dong <yidong@nvidia.com> * seq parallel base Signed-off-by: Yi Dong <yidong@nvidia.com> * recursively process the files Signed-off-by: Yi Dong <yidong@nvidia.com> * add filter filter Signed-off-by: Yi Dong <yidong@nvidia.com> * add the shape file Signed-off-by: Yi Dong <yidong@nvidia.com> * fix the inconsistence in training Signed-off-by: Yi Dong <yidong@nvidia.com> * register optimizers Signed-off-by: Yi Dong <yidong@nvidia.com> * fix the style Signed-off-by: Yi Dong <yidong@nvidia.com> * fix LGTM Signed-off-by: Yi Dong <yidong@nvidia.com> * added model training performance ci test Signed-off-by: Yi Dong <yidong@nvidia.com> * escape properly Signed-off-by: Yi Dong <yidong@nvidia.com> * multiple line quote Signed-off-by: Yi Dong <yidong@nvidia.com> * added python to ci test Signed-off-by: Yi Dong <yidong@nvidia.com> * reduce accuracy check Signed-off-by: Yi Dong <yidong@nvidia.com> * use the same data mapping Signed-off-by: Yi Dong <yidong@nvidia.com> * only test it on A100 gpu Signed-off-by: Yi Dong <yidong@nvidia.com> * added license and use base yaml conf Signed-off-by: Yi Dong <yidong@nvidia.com> * stage 0 Signed-off-by: Yi Dong <yidong@nvidia.com> * add time info Signed-off-by: Yi Dong <yidong@nvidia.com> * fix one bug Signed-off-by: Yi Dong <yidong@nvidia.com> * reduce nlist Signed-off-by: Yi Dong <yidong@nvidia.com> * stage 1 Signed-off-by: Yi Dong <yidong@nvidia.com> * working stage 1 Signed-off-by: Yi Dong <yidong@nvidia.com> * stage 2 Signed-off-by: Yi Dong <yidong@nvidia.com> * fix one bug Signed-off-by: Yi Dong <yidong@nvidia.com> * time add index Signed-off-by: Yi Dong <yidong@nvidia.com> * document data loading time Signed-off-by: Yi Dong <yidong@nvidia.com> * works with CPU only environment Signed-off-by: Yi Dong <yidong@nvidia.com> * add doc string Signed-off-by: Yi Dong <yidong@nvidia.com> * handle gpu faiss Signed-off-by: Yi Dong <yidong@nvidia.com> * added sharding knn index Signed-off-by: Yi Dong <yidong@nvidia.com> * added unit test for sharding index Signed-off-by: Yi Dong <yidong@nvidia.com> * knn stage processing Signed-off-by: Yi Dong <yidong@nvidia.com> * fix shard gpu index Signed-off-by: Yi Dong <yidong@nvidia.com> * add the unit test for dedup logics Signed-off-by: Yi Dong <yidong@nvidia.com> * fix parallel jit Signed-off-by: Yi Dong <yidong@nvidia.com> * added doc Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * use the sharded version Signed-off-by: Yi Dong <yidong@nvidia.com> * fix style Signed-off-by: Yi Dong <yidong@nvidia.com> * fix save checkpoint Signed-off-by: Yi Dong <yidong@nvidia.com> * fix id update Signed-off-by: Yi Dong <yidong@nvidia.com> * move to constructor; Signed-off-by: Yi Dong <doyend@gmail.com> * fix the test Signed-off-by: Yi Dong <doyend@gmail.com> * make checkpoint activation work Signed-off-by: Yi Dong <doyend@gmail.com> * reduce duplicate Signed-off-by: Yi Dong <doyend@gmail.com> * fix selective attention Signed-off-by: Yi Dong <doyend@gmail.com> * fix the style Signed-off-by: Yi Dong <yidong@nvidia.com> * remove the max check Signed-off-by: Yi Dong <yidong@nvidia.com> * added comments Signed-off-by: Yi Dong <doyend@gmail.com> Signed-off-by: Yi Dong <yidong@nvidia.com> Signed-off-by: Yi Dong <doyend@gmail.com> Co-authored-by: Yi Dong <doyend@gmail.com> Co-authored-by: Oleksii Kuchaiev <okuchaiev@users.noreply.github.com> Signed-off-by: Hainan Xu <hainanx@nvidia.com>

yidong72 and others added 30 commits July 5, 2022 21:26

dump shape

6baa5e6

Signed-off-by: Yi Dong <yidong@nvidia.com>

fix the readout layer

68bd1fe

Signed-off-by: Yi Dong <yidong@nvidia.com>

working

760a9cb

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remove the scaling

08ab015

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fix style

c4a370b

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save file by config

d6e1d56

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confirm tp works

ef9ab15

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added support of ckpt conversion

2c96cc7

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add loss scale

5c012a0

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padding only

0c6b656

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Merge branch 'main' into mutransfer

3db284f

add comments, set normfactor in file

0bd944e

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fix style

b0c33bc

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set it norm factor correctly

a867b7c

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match the original one

b7460d7

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fix weight init

9f2d6d3

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hard code the output std

5dc1d7c

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Merge branch 'main' into mutransfer

c9c5a32

seq parallel base

a57326f

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recursively process the files

20f00c4

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add filter filter

d06a427

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add the shape file

3b66a26

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fix the inconsistence in training

a91fb41

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register optimizers

ed56b5e

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fix the style

dbf6270

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fix LGTM

2324792

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Merge branch 'main' into mutransfer

eae96d3

added model training performance ci test

cadcf05

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escape properly

5abb91c

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multiple line quote

ddce82f

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make checkpoint activation work

3826add

Signed-off-by: Yi Dong <doyend@gmail.com>

reduce duplicate

1b1adc2

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fix selective attention

ae42d7d

Signed-off-by: Yi Dong <doyend@gmail.com>

Merge branch 'main' into feature_shard_index

e027c2b

okuchaiev requested a review from soumye August 31, 2022 17:17

fix the style

fd0b19c

Signed-off-by: Yi Dong <yidong@nvidia.com>

Merge branch 'main' into feature_shard_index

8d66ac5

remove the max check

6b4e3a9

Signed-off-by: Yi Dong <yidong@nvidia.com>

michalivne previously approved these changes Sep 6, 2022

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soumye reviewed Sep 6, 2022

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added comments

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Signed-off-by: Yi Dong <doyend@gmail.com>

yidong72 dismissed michalivne’s stale review via c978671 September 7, 2022 00:59

michalivne approved these changes Sep 7, 2022

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yidong72 merged commit 0d7c4bc into main Sep 7, 2022

yidong72 deleted the feature_shard_index branch September 7, 2022 14:37

		@@ -175,6 +264,8 @@ def get_emb():
		default='bert-base-nli-mean-tokens',

Adding RETRO model Faiss sharding index and KNN sharding index #4713

Adding RETRO model Faiss sharding index and KNN sharding index #4713

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yidong72 commented Aug 9, 2022 • edited Loading

What does this PR do ?

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yidong72 commented Aug 9, 2022 •

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