TypeError: 'FullTransformerBatch' object is not iterable

[faisal-hearth]

I'm getting this error while trying to train and update NER.
Error:

TypeError("'FullTransformerBatch' object is not iterable")
Traceback (most recent call last):
  File "C:\Users\kzrdf\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\Users\kzrdf\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\__main__.py", line 4, in <module>
    setup_cli()
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\cli\_util.py", line 69, in setup_cli
    command(prog_name=COMMAND)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\click\core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\click\core.py", line 782, in main
    rv = self.invoke(ctx)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\click\core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\click\core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\click\core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\typer\main.py", line 497, in wrapper
    return callback(**use_params)  # type: ignore
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\cli\train.py", line 59, in train_cli
    train(nlp, output_path, use_gpu=use_gpu, stdout=sys.stdout, stderr=sys.stderr)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\training\loop.py", line 115, in train
    raise e
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\training\loop.py", line 98, in train
    for batch, info, is_best_checkpoint in training_step_iterator:
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\training\loop.py", line 195, in train_while_improving
    nlp.update(
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\language.py", line 1112, in update
    proc.update(examples, sgd=None, losses=losses, **component_cfg[name])
  File "spacy\pipeline\transition_parser.pyx", line 358, in spacy.pipeline.transition_parser.Parser.update
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\thinc\model.py", line 306, in begin_update
    return self._func(self, X, is_train=True)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy\ml\tb_framework.py", line 31, in forward
    step_model = ParserStepModel(
  File "spacy\ml\parser_model.pyx", line 216, in spacy.ml.parser_model.ParserStepModel.__init__
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\thinc\model.py", line 288, in __call__
    return self._func(self, X, is_train=is_train)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\thinc\layers\chain.py", line 54, in forward
    Y, inc_layer_grad = layer(X, is_train=is_train)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\thinc\model.py", line 288, in __call__
    return self._func(self, X, is_train=is_train)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\thinc\layers\chain.py", line 54, in forward
    Y, inc_layer_grad = layer(X, is_train=is_train)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\thinc\model.py", line 288, in __call__
    return self._func(self, X, is_train=is_train)
  File "C:\ZDRIVE\Workspace\DPF\Hearth\Automated_Scraper\spacyWS\projects\.env\lib\site-packages\spacy_transformers\layers\trfs2arrays.py", line 23, in forward
    for trf_data in trf_datas:
TypeError: 'FullTransformerBatch' object is not iterable

Here's the config.cfg I'm using

[paths]
train = "corpus/train.spacy"
dev = "corpus/dev.spacy"
vectors = null
raw = null
init_tok2vec = null
vocab_data = null

[system]
gpu_allocator = "pytorch"
seed = 1

[nlp]
lang = "en"
pipeline = ["transformer","tagger","parser","ner","attribute_ruler","lemmatizer"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
batch_size = 64
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}

[components]

[components.attribute_ruler]
source = "en_core_web_trf"

[components.lemmatizer]
source = "en_core_web_trf"

[components.ner]
source = "en_core_web_trf"
replace_listeners = ["model.tok2vec"]

[components.parser]
source = "en_core_web_trf"

[components.tagger]
source = "en_core_web_trf"

[components.transformer]
source = "en_core_web_trf"

[corpora]

[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
gold_preproc = false
max_length = 0
limit = 0
augmenter = null

[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
gold_preproc = false
max_length = 0
limit = 0
augmenter = null

[training]
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = ["transformer","tagger","parser","attribute_ruler","lemmatizer"]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
before_to_disk = null

[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
get_length = null

[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
t = 0.0

[training.logger]
@loggers = "spacy.ConsoleLogger.v1"
progress_bar = false

[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 0.00000001
learn_rate = 0.001

[training.score_weights]

[pretraining]

[initialize]
vocab_data = ${paths.vocab_data}
vectors = null
init_tok2vec = ${paths.init_tok2vec}
after_init = null
lookups = null

[initialize.before_init]
@callbacks = "spacy.copy_from_base_model.v1"
tokenizer = "en_core_web_trf"
vocab = "en_core_web_trf"

[initialize.components]

[initialize.components.ner]

[initialize.components.ner.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/ner.json"
require = false

[initialize.components.parser]

[initialize.components.parser.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/parser.json"
require = false

[initialize.components.tagger]

[initialize.components.tagger.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/tagger.json"
require = false

[initialize.tokenizer]

[adrianeboyd]

Hi, this is a bug in replace_listeners for the transformer model. We are working on it!

[faisal-hearth]

@adrianeboyd
When can I expect the fix

[svlandeg]

The fix will be available for the next release of spaCy & spacy-transformers. It's currently being developed and will be reviewed soon. Thanks for your patience!

[svlandeg]

cf explosion/spacy-transformers#277

[svlandeg]

Hi all, the fix is available for spacy-transformers>=1.0.5 which requires spacy>=3.1

Thanks again for the report!