Error when training on nightly

I’m getting this error when trying to train on the nightly version of spacy:

... File "/usr/local/lib/python3.6/dist-packages/thinc/model.py", line 288, in __call__ return self._func(self, X, is_train=is_train) File "/usr/local/lib/python3.6/dist-packages/thinc/layers/softmax.py", line 32, in forward W = cast(Floats2d, model.get_param("W")) File "/usr/local/lib/python3.6/dist-packages/thinc/model.py", line 213, in get_param f"Parameter '{name}' for model '{self.name}' has not been allocated yet." KeyError: "Parameter 'W' for model 'softmax' has not been allocated yet."

How to reproduce the behaviour

!python -m spacy train 'config.cfg' --output='model' --gpu-id=0 --verbose --paths.train train.spacy --paths.dev test.spacy

config.cfg:

`[paths]
train = null
dev = null
vectors = null
init_tok2vec = null

[system]
gpu_allocator = "pytorch"
seed = 0

[nlp]
lang = "es"
pipeline = ["transformer","tagger","parser","ner"]
tokenizer = {"@<!-- -->tokenizers":"spacy.Tokenizer.v1"}
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null

[components]

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

[components.ner.model]
@<!-- -->architectures = "spacy.TransitionBasedParser.v1"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null

[components.ner.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@<!-- -->layers":"reduce_mean.v1"}

[components.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 30
moves = null
update_with_oracle_cut_size = 100

[components.parser.model]
@<!-- -->architectures = "spacy.TransitionBasedParser.v1"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = false
nO = null

[components.parser.model.tok2vec]
@<!-- -->architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@<!-- -->layers":"reduce_mean.v1"}

[components.tagger]
factory = "tagger"

[components.tagger.model]
@<!-- -->architectures = "spacy.Tagger.v1"
nO = null

[components.tagger.model.tok2vec]
@<!-- -->architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@<!-- -->layers":"reduce_mean.v1"}

[components.transformer]
factory = "transformer"
max_batch_items = 4096
set_extra_annotations = {"@<!-- -->annotation_setters":"spacy-transformers.null_annotation_setter.v1"}

[components.transformer.model]
@<!-- -->architectures = "spacy-transformers.TransformerModel.v1"
name = "mrm8488/RuPERTa-base"

[components.transformer.model.get_spans]
@<!-- -->span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96

[components.transformer.model.tokenizer_config]
use_fast = true

[corpora]

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

[corpora.train]
@<!-- -->readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 500
gold_preproc = false
limit = 0
augmenter = null

[training]
accumulate_gradient = 3
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = ["tagger", "parser"]
before_to_disk = null

[training.batcher]
@<!-- -->batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
get_length = null

[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

[training.optimizer.learn_rate]
@<!-- -->schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 0.00005

[training.score_weights]
dep_las_per_type = null
sents_p = null
sents_r = null
ents_per_type = null
tag_acc = 0.33
dep_uas = 0.17
dep_las = 0.17
sents_f = 0.0
ents_f = 0.33
ents_p = 0.0
ents_r = 0.0

[pretraining]

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

[initialize.components]

[initialize.tokenizer]`

Any clues on what may be happening are appreciated.

Your Environment

• spaCy version: 3.0.0a35
• Platform: Linux-4.19.112±x86_64-with-Ubuntu-18.04-bionic
• Python version: 3.6.9
• Pipelines: es_core_news_md (3.0.0a0)

[svlandeg]

Awesome, happy to hear it works now!

[svlandeg]

I just noticed the frozen components. I think there’s a bug related to those, that they are not disabled when calling the evaluation. I’ll have a further look. Your stack trace is helpful, thanks 😃