[FEATURE] Add Support For `TextGenerationPipeline`

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Problem Statement

At present, we have support for StableDiffusionPipelines but lack support for TextGenerationPipeline. This limitation hampers the workflow of working with Pruna, as we need to manually encode and decode all inputs and outputs. This not only adds complexity but also slows down the development process.

Solution Overview

We propose to add support for TextGenerationPipeline in Pruna, mirroring the flow outlined in the Pruna documentation. This will enable seamless integration with Pruna, making it easier to work with the library.

Solution Details

To achieve this, we will follow a similar approach as outlined in the Pruna documentation. We will create a TextGenerationPipeline object using the pipeline function from the transformers library. This object will be used to generate text based on a given input.

Here's an example code snippet that demonstrates the proposed solution:

from transformers import pipeline

# Create a TextGenerationPipeline object
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Define a list of messages
messages = [
    {"role": "user", "content": "Who are you?"},
]

# Use the pipeline to generate text
output = pipe(messages, max_new_tokens=100)

print(output)

Pruna Integration

Once we have the TextGenerationPipeline object, we can integrate it with Pruna using the SmashConfig and smash functions. This will enable us to quantize the model and reduce its size while maintaining its accuracy.

Here's an example code snippet that demonstrates the Pruna integration:

from pruna import SmashConfig

# Create a SmashConfig object
smash_config = SmashConfig(device="mps")
smash_config["quantizer"] = "hqq"

# Use the smash function to quantize the model
smashed_model = smash(
    model=pipe,
    smash_config=smash_config,
)

Benefits

The proposed solution will bring several benefits, including:

  • Simplified workflow: With support for TextGenerationPipeline, we can simplify the workflow of working with Pruna, making it easier to develop and deploy models.
  • Improved performance: By integrating Pruna with TextGenerationPipeline, we can improve the performance of the model, reducing its size while maintaining its accuracy.
  • Enhanced user experience: The proposed solution will provide a better user experience, as users will no longer need to manually encode and decode inputs and outputs.

Implementation Plan

To implement the proposed solution, we will follow these steps:

  1. Add support for TextGenerationPipeline: We will add support for TextGenerationPipeline in Pruna, mirroring the flow outlined in the Pruna documentation.
  2. Integrate with Pruna: We will integrate the TextGenerationPipeline object with Pruna using the SmashConfig and smash functions.
  3. Test and validate: We will test and validate the proposed solution to ensure that it works as expected.

Timeline

We estimate that the implementation will take approximately 2-3 weeks, depending on the complexity of the implementation.

Conclusion

In conclusion, adding support for TextGenerationPipeline in Pruna will simplify the workflow of working with the library, improve performance, and enhance the user experience. We believe that this is a crucial feature that will benefit the Pruna community, and we are excited to implement it.

Future Work

Once we have implemented the proposed solution, we plan to explore the following future work:

  • Add support for other pipelines: We plan to add support for other pipelines, such as ImageGenerationPipeline and SpeechGenerationPipeline.
  • Improve performance: We plan to improve the performance of the model by exploring different quantization techniques and optimizing the model architecture.
  • Enhance user experience: We plan to enhance the user experience by providing more features and tools for working with Pruna.
    Q&A: Adding Support for TextGenerationPipeline in Pruna =====================================================

Frequently Asked Questions

We've received several questions about adding support for TextGenerationPipeline in Pruna. Here are some of the most frequently asked questions and their answers:

Q: What is TextGenerationPipeline and why do we need it?

A: TextGenerationPipeline is a type of pipeline that generates text based on a given input. We need it because it will simplify the workflow of working with Pruna, making it easier to develop and deploy models.

Q: How will adding support for TextGenerationPipeline improve performance?

A: Adding support for TextGenerationPipeline will improve performance by reducing the size of the model while maintaining its accuracy. This is achieved through quantization, which replaces floating-point numbers with smaller integers.

Q: What are the benefits of using TextGenerationPipeline in Pruna?

A: The benefits of using TextGenerationPipeline in Pruna include:

  • Simplified workflow: With support for TextGenerationPipeline, we can simplify the workflow of working with Pruna, making it easier to develop and deploy models.
  • Improved performance: By integrating Pruna with TextGenerationPipeline, we can improve the performance of the model, reducing its size while maintaining its accuracy.
  • Enhanced user experience: The proposed solution will provide a better user experience, as users will no longer need to manually encode and decode inputs and outputs.

Q: How will you implement the proposed solution?

A: We will implement the proposed solution by adding support for TextGenerationPipeline in Pruna, mirroring the flow outlined in the Pruna documentation. We will then integrate the TextGenerationPipeline object with Pruna using the SmashConfig and smash functions.

Q: What is the timeline for implementing the proposed solution?

A: We estimate that the implementation will take approximately 2-3 weeks, depending on the complexity of the implementation.

Q: What are the next steps after implementing the proposed solution?

A: After implementing the proposed solution, we plan to:

  • Test and validate: We will test and validate the proposed solution to ensure that it works as expected.
  • Add support for other pipelines: We plan to add support for other pipelines, such as ImageGenerationPipeline and SpeechGenerationPipeline.
  • Improve performance: We plan to improve the performance of the model by exploring different quantization techniques and optimizing the model architecture.
  • Enhance user experience: We plan to enhance the user experience by providing more features and tools for working with Pruna.

Q: How will the proposed solution benefit the Pruna community?

A: The proposed solution will benefit the Pruna community by:

  • Simplifying the workflow: With support for TextGenerationPipeline, we can simplify the workflow of working with Pruna, making it easier to develop and deploy models.
  • Improving performance: By integrating Pruna with TextGenerationPipeline, we can improve the performance of the model, reducing its size while maintaining its.
  • Enhancing user experience: The proposed solution will provide a better user experience, as users will no longer need to manually encode and decode inputs and outputs.

Conclusion

In conclusion, adding support for TextGenerationPipeline in Pruna will simplify the workflow of working with the library, improve performance, and enhance the user experience. We believe that this is a crucial feature that will benefit the Pruna community, and we are excited to implement it.