explosion / spaCy

💫 Industrial-strength Natural Language Processing (NLP) in Python
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Issues with resuming training in version 3.1.2 #9192

Open Julia-Penfield opened 3 years ago

Julia-Penfield commented 3 years ago

I am trying to continue training an NER model. I have done this in spaCy 3.0.6 in the past numerous times, but after coming back to do it again, I realized that this time it's not working. My current version is 3.1.2.

My approach to continued training:

To continue training, I basically modified the config file. The 2 components below are changed to the ones following:

Initial config file setup:

[components.ner]
factory = "ner"
moves = null
update_with_oracle_cut_size = 100

[components.tok2vec]
factory = "tok2vec"

Modified config file:

[components.ner]
source = "./saved_model/model-last"
#factory = "ner"
#moves = null
#update_with_oracle_cut_size = 100

[components.tok2vec]
source = "./saved_model/model-last"
#factory = "tok2vec"

"./saved model" is the address to which the initial training is saved.

I also tried with the following config file setups:

[components.ner]
source = "./fake_output_0_backup/model-last"
component = "ner"
#factory = "ner"
#moves = null
#update_with_oracle_cut_size = 100

[components.tok2vec]
source = "./fake_output_0_backup/model-last"
component = "tok2vec"
#factory = "tok2vec"

This approach seem to not work anymore as I get the following error:

/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/training/initialize.py:137: UserWarning: [W113] Sourced component 'tok2vec' may not work as expected: source vectors are not identical to current pipeline vectors.
  warnings.warn(Warnings.W113.format(name=sourced_component))
/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/training/initialize.py:137: UserWarning: [W113] Sourced component 'ner' may not work as expected: source vectors are not identical to current pipeline vectors.
  warnings.warn(Warnings.W113.format(name=sourced_component))
[2021-09-13 00:01:58,021] [INFO] Finished initializing nlp object
[2021-09-13 00:01:58,021] [INFO] Initialized pipeline components: []
*** Error in `/home/ec2-user/anaconda3/envs/python3/bin/python': corrupted size vs. prev_size: 0x00005617d85ceba0 *** 

Question: Has something changed in the recent versions to disable this approach?

I can send the backtrace if it could be helpful, but beware that it's super long and I could not learn the issue from it. :)

I would appreciate any recommendations. I can try in spaCy 3.0.6 if it helps to verify that it's a version issue.

My Environment

polm commented 3 years ago

Thanks for the report, we'll take a look at that!

To be clear, this is a model that you trained in 3.1.2 initially, and then resumed training on? Or did you initially train this with 3.0.6 and then try to resume in 3.1.2?

Also if you could go ahead and provide the whole stack trace that'd be helpful. You can put it behind a <details> tag so it's hidden by default.

Julia-Penfield commented 3 years ago

Hi @polm,

Thanks for the reply.

Regarding the version:

The model that I am working on and reported the issue about its continued training is only trained in 3.1.2 - both the initial and the continuation attempt. I have not tried resuming training of a model that is initially trained in 3.0.6, but I will likely give it a try soon.

Regarding the the stack trace

There are two separate cases that I put here.

First version:

If the config file is setup as:

[components.ner] source = "./saved_model/model-last" component = "ner"

factory = "ner"

moves = null

update_with_oracle_cut_size = 100

[components.tok2vec] source = "./saved_model/model-last" component = "tok2vec"

factory = "tok2vec"

In this case, the stack trace is short:

[2021-09-13 00:42:10,986] [INFO] Set up nlp object from config [2021-09-13 00:42:10,996] [INFO] Pipeline: ['tok2vec', 'ner'] [2021-09-13 00:42:10,996] [INFO] Resuming training for: ['ner', 'tok2vec'] [2021-09-13 00:42:11,000] [INFO] Created vocabulary /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/training/initialize.py:137: UserWarning: [W113] Sourced component 'tok2vec' may not work as expected: source vectors are not identical to current pipeline vectors. warnings.warn(Warnings.W113.format(name=sourced_component)) /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/training/initialize.py:137: UserWarning: [W113] Sourced component 'ner' may not work as expected: source vectors are not identical to current pipeline vectors. warnings.warn(Warnings.W113.format(name=sourced_component)) [2021-09-13 00:42:11,001] [INFO] Finished initializing nlp object [2021-09-13 00:42:11,001] [INFO] Initialized pipeline components: []

ℹ Saving to output directory: saved_model ℹ Using CPU

=========================== Initializing pipeline =========================== ✔ Initialized pipeline

============================= Training pipeline ============================= ℹ Pipeline: ['tok2vec', 'ner'] ℹ Initial learn rate: 0.001 E # LOSS TOK2VEC LOSS NER ENTS_F ENTS_P ENTS_R SCORE


Second version:

If the config file is setup like this:

[components.ner] source = "./saved_model/model-last"

factory = "ner"

moves = null

update_with_oracle_cut_size = 100

[components.tok2vec] source = "./saved_model/model-last"

factory = "tok2vec"

Then the stack trace is very long as shown below.

[2021-09-13 16:53:30,824] [INFO] Set up nlp object from config [2021-09-13 16:53:30,834] [INFO] Pipeline: ['tok2vec', 'ner'] [2021-09-13 16:53:30,835] [INFO] Resuming training for: ['ner', 'tok2vec'] [2021-09-13 16:53:30,838] [INFO] Created vocabulary /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/training/initialize.py:137: UserWarning: [W113] Sourced component 'tok2vec' may not work as expected: source vectors are not identical to current pipeline vectors. warnings.warn(Warnings.W113.format(name=sourced_component)) /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/training/initialize.py:137: UserWarning: [W113] Sourced component 'ner' may not work as expected: source vectors are not identical to current pipeline vectors. warnings.warn(Warnings.W113.format(name=sourced_component)) [2021-09-13 16:53:30,839] [INFO] Finished initializing nlp object [2021-09-13 16:53:30,839] [INFO] Initialized pipeline components: [] *** Error in `/home/ec2-user/anaconda3/envs/python3/bin/python': corrupted size vs. prev_size: 0x000055e2871076c0 *** ======= Backtrace: ========= /lib64/libc.so.6(+0x7f434)[0x7f295c025434] /lib64/libc.so.6(+0x82c44)[0x7f295c028c44] /lib64/libc.so.6(__libc_malloc+0x4c)[0x7f295c02b74c] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xd5867)[0x55e25cbd1867] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x12359c)[0x55e25cc1f59c] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x13da20)[0x55e25cc39a20] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1066)[0x55e25cca0ac6] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x1412a9)[0x55e25cc3d2a9] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x142f9f)[0x55e25cc3ef9f] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x424)[0x55e25cc9fe84] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyFunction_FastCallDict+0x562)[0x55e25cbe82e2] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x153ad3)[0x55e25cc4fad3] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x1749b8)[0x55e25cc709b8] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyObject_FastCallKeywords+0x193)[0x55e25cc3e7a3] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x1431d5)[0x55e25cc3f1d5] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x424)[0x55e25cc9fe84] /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/pipeline/trainable_pipe.cpython-36m-x86_64-linux-gnu.so(+0xef88)[0x7f286d0fef88] /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/pipeline/trainable_pipe.cpython-36m-x86_64-linux-gnu.so(+0x10ade)[0x7f286d100ade] /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/pipeline/trainable_pipe.cpython-36m-x86_64-linux-gnu.so(+0x12326)[0x7f286d102326] /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/pipeline/trainable_pipe.cpython-36m-x86_64-linux-gnu.so(+0x201ec)[0x7f286d1101ec] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x14306a)[0x55e25cc3f06a] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x424)[0x55e25cc9fe84] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x17f8a0)[0x55e25cc7b8a0] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x850)[0x55e25cca02b0] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x141488)[0x55e25cc3d488] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x142f9f)[0x55e25cc3ef9f] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1308)[0x55e25cca0d68] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x190e8f)[0x55e25cc8ce8f] /home/ec2-user/anaconda3/envs/python3/bin/python(PyObject_Call+0x3e)[0x55e25cbf77fe] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1c61)[0x55e25cca16c1] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x190e8f)[0x55e25cc8ce8f] /home/ec2-user/anaconda3/envs/python3/bin/python(PyObject_Call+0x3e)[0x55e25cbf77fe] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1c61)[0x55e25cca16c1] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyFunction_FastCallDict+0x784)[0x55e25cbe8504] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x153ad3)[0x55e25cc4fad3] /home/ec2-user/anaconda3/envs/python3/bin/python(PyObject_Call+0x3e)[0x55e25cbf77fe] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1c61)[0x55e25cca16c1] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x1412a9)[0x55e25cc3d2a9] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x142f9f)[0x55e25cc3ef9f] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x424)[0x55e25cc9fe84] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x141488)[0x55e25cc3d488] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x142f9f)[0x55e25cc3ef9f] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x424)[0x55e25cc9fe84] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyFunction_FastCallDict+0x784)[0x55e25cbe8504] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x153ad3)[0x55e25cc4fad3] /home/ec2-user/anaconda3/envs/python3/bin/python(PyObject_Call+0x3e)[0x55e25cbf77fe] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1c61)[0x55e25cca16c1] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyFunction_FastCallDict+0x784)[0x55e25cbe8504] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x153ad3)[0x55e25cc4fad3] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x174bae)[0x55e25cc70bae] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyObject_FastCallKeywords+0x193)[0x55e25cc3e7a3] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x1431d5)[0x55e25cc3f1d5] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x1308)[0x55e25cca0d68] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x1412a9)[0x55e25cc3d2a9] /home/ec2-user/anaconda3/envs/python3/bin/python(+0x142f9f)[0x55e25cc3ef9f] /home/ec2-user/anaconda3/envs/python3/bin/python(_PyEval_EvalFrameDefault+0x424)[0x55e25cc9fe84] /home/ec2-user/anaconda3/envs/python3/bin/python(+0xea8f5)[0x55e25cbe68f5] ======= Memory map: ======== 55e25cafc000-55e25cb53000 r--p 00000000 103:03 3552926 /home/ec2-user/anaconda3/envs/python3/bin/python3.6 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ℹ Saving to output directory: saved_model ℹ Using CPU =========================== Initializing pipeline =========================== ✔ Initialized pipeline ============================= Training pipeline ============================= ℹ Pipeline: ['tok2vec', 'ner'] ℹ Initial learn rate: 0.001 E # LOSS TOK2VEC LOSS NER ENTS_F ENTS_P ENTS_R SCORE --- ------ ------------ -------- ------ ------ ------ ------ ## One last thing just in case in matters: In the initial training, in the config file, I have: [components.tok2vec.model.embed] @architectures = "spacy.MultiHashEmbed.v2" width = ${components.tok2vec.model.encode.width} attrs = ["ORTH","SHAPE"] rows = [5000,2500] include_static_vectors = true [initialize] vectors = "./wordvec" init_tok2vec = ${paths.init_tok2vec} vocab_data = null lookups = null before_init = null after_init = null [initialize.components] [initialize.tokenizer] where "./wordvec" is the address of my pre-trained word verctors. In the config file to resume training, I have: [components.tok2vec.model.embed] @architectures = "spacy.MultiHashEmbed.v2" width = ${components.tok2vec.model.encode.width} attrs = ["ORTH","SHAPE"] rows = [5000,2500] include_static_vectors = true [initialize] vectors = null init_tok2vec = ${paths.init_tok2vec} vocab_data = null lookups = null before_init = null after_init = null [initialize.components] [initialize.tokenizer] Thank you for your support!
Julia-Penfield commented 3 years ago

Hi @polm,

Just a mini-update that I down-versioned my spaCy and tried the same thing with 3.0.6 and it worked successfully. So I can confirm that the issue of continuation of training is a problem related to an upgrade after 3.0.6.

Cheers, Julia

polm commented 3 years ago

Thanks for the extra detail! Sorry it's taking us a while to get to this - we haven't forgotten about it.