Mem usage issue 9516

[markus-hinsche]

#9516 reports an issue in: cvf_diet_responset2t.yml

• Sparse + DIET(seq) + ResponseSelector(t2t)

Steps:

• confirm this is reproducible -> A first run suggests this is always happen with this dataset-config combination
• examine logs (on DD, on GCP?)
• re-run test -> done with repetitions
• plot nvidia-smi logs to narrow down the increase -> Done with Datadog
• (if relevant) document steps used as an extension of this for future reference

Proposed changes:

• ...

Status (please check what you already did):

• added some tests for the functionality
• updated the documentation
• updated the changelog (please check changelog for instructions)
• reformat files using black (please check Readme for instructions)

[github-actions]

Status of the run: Succeeded

Commit: 9c304a7, The full report is available as an artifact.

Datadog dashboard link

Dataset: Hermit, Dataset repository branch: main, commit: 819cb7b3cc077753e67178ad022d577f164e99cf

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
Sparse + DIET(seq) + ResponseSelector(t2t) test: 1m47s, train: 17m20s, total: 19m6s	0.8327 (no data)	0.9264 (no data)	no data
Sparse + DIET(seq) + ResponseSelector(t2t) test: 1m47s, train: 17m40s, total: 19m27s	0.8374 (no data)	0.9278 (no data)	no data
Sparse + DIET(seq) + ResponseSelector(t2t) test: 1m46s, train: 17m36s, total: 19m21s	0.8346 (no data)	0.9260 (no data)	no data

[github-actions]

Hey @markus-hinsche! 👋 To run model regression tests, comment with the /modeltest command and a configuration.

Tips 💡: The model regression test will be run on push events. You can re-run the tests by re-add status:model-regression-tests label or use a Re-run jobs button in Github Actions workflow.

Tips 💡: Every time when you want to change a configuration you should edit the comment with the previous configuration.

You can copy this in your comment and customize:

/modeltest

```yml
##########
## Available datasets
##########
# - "Carbon Bot" (NLU)
# - "Hermit" (NLU)
# - "Private 1" (NLU)
# - "Private 2" (NLU)
# - "Private 3" (NLU)
# - "Sara" (NLU, Core)
# - "financial-demo" (NLU, Core)
# - "helpdesk-assistant" (NLU, Core)
# - "insurance-demo" (NLU, Core)
# - "retail-demo" (NLU, Core)

##########
## Available NLU configurations
##########
# - "BERT + DIET(bow) + ResponseSelector(bow)"
# - "BERT + DIET(seq) + ResponseSelector(t2t)"
# - "Spacy + DIET(bow) + ResponseSelector(bow)"
# - "Spacy + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + BERT + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + BERT + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + Spacy + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)"

##########
## Available Core configurations
##########
# - "Rules"
# - "Rules + AugMemo"
# - "Rules + AugMemo + TED"
# - "Rules + Memo"
# - "Rules + Memo + TED"
# - "Rules + TED"

## Example configuration
#################### syntax #################
## include:
##   - dataset: ["<dataset_name>"]
##     config: ["<configuration_name>"]
#
## Example:
## include:
##  - dataset: ["Carbon Bot"]
##    config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]
#
## Shortcut:
## You can use the "all" shortcut to include all available configurations or datasets
#
## Example: Use the "Sparse + EmbeddingIntent + ResponseSelector(bow)" configuration
## for all available datasets
## include:
##  - dataset: ["all"]
##    config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]
#
## Example: Use all available configurations for the "Carbon Bot" and "Sara" datasets
## and for the "Hermit" dataset use the "Sparse + DIET + ResponseSelector(T2T)" and
## "BERT + DIET + ResponseSelector(T2T)" configurations:
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["all"]
##  - dataset: ["Hermit"]
##    config: ["Sparse + DIET(seq) + ResponseSelector(t2t)", "BERT + DIET(seq) + ResponseSelector(t2t)"]
#
## Example: Define a branch name to check-out for a dataset repository. Default branch is 'main'
## dataset_branch: "test-branch"
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["all"]
#
## Example: Define number of repetitions. This will inform how often to repeat all runs defined in the include section. Default is 1
## num_repetitions: 2
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["Sparse + DIET(seq) + ResponseSelector(t2t)"]
##
## Shortcuts:
## You can use the "all" shortcut to include all available configurations or datasets.
## You can use the "all-nlu" shortcut to include all available NLU configurations or datasets.
## You can use the "all-core" shortcut to include all available core configurations or datasets.

include:
 - dataset: ["Carbon Bot"]
   config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]

```

[github-actions]

/modeltest

num_repetitions: 3
include:
 - dataset: ["Hermit"]
   config: ["Sparse + DIET(seq) + ResponseSelector(t2t)"]

[github-actions]

The model regression tests have started. It might take a while, please be patient.
As soon as results are ready you'll see a new comment with the results.

Used configuration can be found in the comment.

[github-actions]

Status of the run: Failed

Commit: 9c304a7, The full report is available as an artifact.

Datadog dashboard link

Dataset: Hermit, Dataset repository branch: main, commit: 819cb7b3cc077753e67178ad022d577f164e99cf

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
Sparse + DIET(seq) + ResponseSelector(t2t) test: 1m36s, train: 3h14m9s, total: 3h15m44s	0.8392 (no data)	0.9279 (no data)	no data