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1. Beyond Autocomplete:
Local AI Code Completion Demystified

—
Daniel Savenkov, Senior ML Engineer at JetBrains

5. AI Code Completion 101

6. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101

7. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101
Local AI Code Completion by JetBrains: Details

8. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101
Local AI Code Completion by JetBrains: Details
Local AI Code Completion by JetBrains: Team

9. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101
Local AI Code Completion by JetBrains: Details
Local AI Code Completion by JetBrains: Team

13. Needs Cloud to operate
Typical AI Code Completion
Use Large Language Model

14. Needs Cloud to operate
Typical AI Code Completion
Use Large Language Model
User Typing Code Smart Suggestions
Large Language Model
in Cloud

15. Typical AI Code Completion
Can use really big models
Pros:
User Typing Code Smart Suggestions
Large Language Model
in Cloud

16. Typical AI Code Completion
Can use really big models
Pros:
No additional computations on
user side
User Typing Code Smart Suggestions
Large Language Model
in Cloud

17. Typical AI Code Completion
Can use really big models
Pros:
No additional computations on
user side
Cons:
Code is sent to the Internet
(poor security, poor latency)
User Typing Code Smart Suggestions
Large Language Model
in Cloud

18. Typical AI Code Completion
Can use really big models
Pros:
No additional computations on
user side
Cons:
Code is sent to the Internet
(poor security, poor latency)
Someone has to pay for the cloud
(either users or the company)
User Typing Code Smart Suggestions
Large Language Model
in Cloud

19. Can we go local?

20. Cmooon
You need me
Can we go local?

21. Ok ok
I’ll go away
Can we go local?

22. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101
Local AI Code Completion by JetBrains: Details
Local AI Code Completion by JetBrains: Team

23. Full Line Code Completion by JetBrains

24. Full Line Code Completion by JetBrains
Local AI

25. Full Line Code Completion by JetBrains
Local AI
Uses tiny language model that runs on your laptop

26. Full Line Code Completion by JetBrains
Local AI
Uses tiny language model that runs on your laptop
Available out of the box in JetBrains IDEs

27. Full Line Code Completion by JetBrains
Local AI
Uses tiny language model that runs on your laptop
Available out of the box in JetBrains IDEs
Saves a vast amount of typing

28. Full Line Code Completion by JetBrains
Local AI
Uses tiny language model that runs on your laptop
Available out of the box in JetBrains IDEs
Saves a vast amount of typing
Checks semantic correctness of the generated code

29. Full Line Code Completion by JetBrains
Local AI
Uses tiny language model that runs on your laptop
Available out of the box in JetBrains IDEs
Saves a vast amount of typing
Checks semantic correctness of the generated code
Does not require any additional subsciption

30. Full Line Code Completion by JetBrains: feedback

31. Full Line Code Completion by JetBrains: feedback

32. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101
Local AI Code Completion by JetBrains: Details
Local AI Code Completion by JetBrains: Team

33. User Typing Code Smart Suggestions
Language Model

34. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

35. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

36. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

37. Measuring the code completion

38. How do we make sure that adding Full Line Completion
improves user experience?

39. JetBrains Early Access Program
AB-testing
Full Line enabled Full Line disabled

40. + Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users
AB-testing

41. + Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users
How it usually happens

42. + Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users
How it happens at JetBrains

43. Fraction of code generated
AB-testing
+ Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users

44. Fraction of code generated
Explicit cancel rate
AB-testing
+ Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users

45. Fraction of code generated
Deletion after accepting rate
Explicit cancel rate
AB-testing
+ Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users

46. IDE performance degradation
AB-testing
Fraction of code generated
Deletion after accepting rate
Explicit cancel rate
+ Positive signal: Users
are enjoying new feature
- Negative signal: New
feature annoys users

47. Full Line Code Completion AB-sets results
Fraction of code
generated
+ 30-100%

48. Full Line Code Completion AB-sets results
Fraction of code
generated
+ 30-100%
Explicit
cancel rate
didn’t change
Deletion after
accepting rate
IDE
performance
didn’t change
didn’t change

49. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

50. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

51. Annoying suggestions: invalid code

52. Suggestions postprocessing: invalid code filtering
Suggestion
Postprocessing
Smart Suggestions
We show suggestion only after it was
checked by IDE correctness checks

53. Suggestions postprocessing: invalid code filtering
Suggestion
Postprocessing
Smart Suggestions
We show suggestion only after it was
checked by IDE correctness checks
User never sees semantically
incorrect code (wrong API calls,
non-existent variables, etc)
Pros:

54. Suggestions postprocessing: invalid code filtering
Suggestion
Postprocessing
Smart Suggestions
We show suggestion only after it was
checked by IDE correctness checks
User never sees semantically
incorrect code (wrong API calls,
non-existent variables, etc)
Pros: Cons:
Correctness check may time-out

55. Suggestions postprocessing: invalid code filtering
Suggestion
Postprocessing
Smart Suggestions
We show suggestion only after it was
checked by IDE correctness checks
User never sees semantically
incorrect code (wrong API calls,
non-existent variables, etc)
Pros: Cons:
Correctness check may time-out
No suggestions while indexing for
some languages (e.g. Java, Kotlin)

56. Reduce annoying suggestions: smart filtering

57. Reduce annoying suggestions: smart filtering

58. Reduce annoying suggestions: smart filtering
Language model hasn’t seen any of user actions

59. Reduce annoying suggestions: smart filtering
Language model hasn’t seen any of user actions
Language model can’t predict whether user will accept the suggestion

60. Language Model Input
Reduce annoying suggestions: smart filtering
for i in
range(10):
range(5):
numbers_list:
Language Model Predictions
Language model hasn’t seen any of user actions
Language model can’t predict whether user will accept the suggestion

61. Predict probability of suggestion acceptance?
Features to predict probability of acceptance:

62. Predict probability of suggestion acceptance?
Features to predict probability of acceptance:
Language model’s output probability

63. Predict probability of suggestion acceptance?
Features to predict probability of acceptance:
Language model’s output probability Length of the context

64. Predict probability of suggestion acceptance?
Features to predict probability of acceptance:
Language model’s output probability Length of the context
User typing speed

65. Predict probability of suggestion acceptance?
Features to predict probability of acceptance:
Language model’s output probability Length of the context
User typing speed
Entity under the caret (method/variable/…)

66. Predict probability of suggestion acceptance?
Features to predict probability of acceptance:
Language model’s output probability Length of the context
User typing speed
Entity under the caret (method/variable/…)

67. Filter Model
Language Model
Opportunity to use paid
JetBrains products for free
User Typing Code
User Actions: Early Access Program

68. Filter Model
Language Model
Opportunity to use paid
JetBrains products for free
User Typing Code
User Actions: Early Access Program
More logs are collected
(code is never collected to logs)

69. Language Model
Opportunity to use paid
JetBrains products for free
Filter Model User Typing Code
User Actions: Early Access Program
More logs are collected
(code is never collected to logs)
Can train model to predict suggestion
acceptance using the features describing
the context and the generated suggestion

70. -Acceptance rate increases by 20-50%
-Explicit cancel rate decreases by 50% and
becomes equal to the baseline (no Full
Line) level
-Total number of selection drops by 20%
Introducing filter model
Acceptance
rate
+ 20-50%

71. -Acceptance rate increases by 20-50%
-Explicit cancel rate decreases by 50% and
becomes equal to the baseline (no Full
Line) level
-Total number of selection drops by 20%
Introducing filter model
Acceptance
rate
Explicit
cancel
rate
- 50%
No full line
With filter model
Without filter model
+ 20-50%

72. -Acceptance rate increases by 20-50%
-Explicit cancel rate decreases by 50% and
becomes equal to the baseline (no Full
Line) level
-Total number of selection drops by 20%
Introducing filter model
Acceptance
rate
Explicit
cancel
rate
Total
number of
selection
- 50%
No full line
With filter model
Without filter model
- 20%
+ 20-50%

73. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

74. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

75. Model Inference
(execution)
Model inference
Language model is not an
executable

76. Model Inference
(execution)
Model inference
Efficient inference of language models is a rapidly developing field
Language model is not an
executable

77. Model Inference
(execution)
Model inference
We use native low-level open-source inference engine written in c++
to ensure efficient inference
Efficient inference of language models is a rapidly developing field
Language model is not an
executable

78. Model inference: llama.cpp progress
Generation time using open-source tools rapidly improves

79. Model inference: llama.cpp progress
Generation time using open-source tools rapidly improves

Spring 2023 Fall 2023
Full Line generation time
with llama.cpp, ms
50
100

80. We are contributing back to llama.cpp =)
Model inference: llama.cpp progress
Generation time using open-source tools rapidly improves
Llama.cpp provides us:
The most efficient generation
Out-of-the-box usage of GPU
on Apple Silicon (M1/M2/M3)
Spring 2023 Fall 2023
50
100
Full Line generation time
with llama.cpp, ms

81. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

82. Language Model
Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching

83. Model
We train language models from scratch

84. Model
We use programming language specific models
We train language models from scratch

85. Model
We use programming language specific models
We use 100M LLama model with 1536 context size
We train language models from scratch

86. Model
We use programming language specific models
We use 100M LLama model with 1536 context size
The model occupies 50-500MB of RAM depending on the
context (model parameters are quantized to 4 bits)
We train language models from scratch

87. Model
We use programming language specific models
We use 100M LLama model with 1536 context size
The model occupies 50-500MB of RAM depending on the
context (model parameters are quantized to 4 bits)
Models are trained on AWS instances with 8 A100 GPUs
We train language models from scratch

88. Model
We use programming language specific models
We use 100M LLama model with 1536 context size
The model occupies 50-500MB of RAM depending on the
context (model parameters are quantized to 4 bits)
Models are trained on AWS instances with 8 A100 GPUs
Full training for one model costs ~$5000
We train language models from scratch

89. Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Inference
(execution)
Suggestion
Postprocessing
User Typing Code Smart Suggestions
Model Training
Triggering Logic
Prompt
Construction
Caching
Language Model

90. User Typing Code Smart Suggestions
Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Training
Model Inference
(execution)
Suggestion
Postprocessing
Triggering Logic
Prompt
Construction
Caching
Language Model

91. Offline
Evaluation
Evaluation of the language model
Evaluation of the entire pipeline

92. Evaluation of the entire pipeline
Trigger code completion
in random places in file

93. Evaluation of the entire pipeline
Trigger code completion
in random places in file
The entire generation
pipeline is executed

94. Evaluation of the entire pipeline
Trigger code completion
in random places in file
The entire generation
pipeline is executed
Metrics

95. Evaluation of the entire pipeline
Trigger code completion
in random places in file
The entire generation
pipeline is executed
Metrics
Suggestions - ground truth match

96. Evaluation of the entire pipeline
Trigger code completion
in random places in file
The entire generation
pipeline is executed
Metrics
Suggestions - ground truth match Latency

97. User Typing Code Smart Suggestions
Offline
Evaluation
Online
Evaluation
And
Monitoring
Model Training
Model Inference
(execution)
Suggestion
Postprocessing
Triggering Logic
Prompt
Construction
Caching
Language Model

98. Local AI Code Completion by JetBrains: Bird-eye View
AI Code Completion 101
Local AI Code Completion by JetBrains: Details
Local AI Code Completion by JetBrains: Team

99. Team
14 people:
3 ML engineers 4 full-stack engineers
(ML + kotlin)
3 kotlin developers
1 c++ developer 0.5 QA 1 Product Manager 1 Team Lead

100. Takeaways

101. Takeaways
Local AI is rapidly developing

102. Takeaways
Local AI is rapidly developing
Making good AI code completion is much more than deploying
a good language model

103. Takeaways
Local AI is rapidly developing
Making good AI code completion is much more than deploying
a good language model
Using local AI models instead of cloud
makes your development process more secure

104. Takeaways
Local AI is rapidly developing
Making good AI code completion is much more than deploying
a good language model
Using local AI models instead of cloud
makes your development process more secure
It’s still possible to do a good AI code completion
based on local AI models

105. Beyond Autocomplete: Local AI Code Completion Demystified
Daniel Savenkov, Senior ML Engineer at JetBrains
—
Full Line Code Completion Arxiv Paper
(December 2023)