Mutable.ai logoAuto Wiki by Mutable.ai
Create your own wiki
AI-generated instantly
Updates automatically
Solo and team plans
Create your own wiki
AI-generated instantly
Updates automatically
Solo and team plans

mistral-src

Auto-generated from mistralai/mistral-src by Mutable.ai Auto WikiRevise

mistral-src
GitHub Repository
Developermistralai
Written inJupyter Notebook
Stars8.5k
Watchers109
Created09/27/2023
Last updated04/03/2024
LicenseApache License 2.0
Homepagemistral.ai
Repositorymistralai/mistral-src
Auto Wiki
Revision
Software Version0.0.8Basic
Generated fromCommit 8598cf
Generated at04/03/2024

The mistral-src repository contains the core implementation of the Mistral Transformer-based language model, which provides advanced features like Rotary Positional Embeddings, Mixture-of-Experts (MoE) layers, and pipeline parallelism. This reference implementation can be used by engineers to build and deploy large language models with state-of-the-art capabilities.

The most important parts of the repository are the mistral directory, which contains the Transformer model implementation, and the deploy directory, which handles the setup and configuration for running the Mistral AI application.

The mistral directory includes the following key components:

  • The Attention module, which implements the attention mechanism used in the Transformer model, applying Rotary Positional Embeddings and utilizing the memory_efficient_attention() function from the xformers library.
  • The FeedForward module, which implements the feed-forward neural network used in the Transformer model.
  • The RMSNorm module, which implements the RMS normalization used in the Transformer model.
  • The TransformerBlock module, which combines the Attention and FeedForward modules, along with the RMSNorm layers, to create a single Transformer block.
  • The Transformer module, which is the main entry point for the Transformer model, handling pipeline parallelism and overriding the load_state_dict() method to support loading model parameters in a pipeline-parallel setup.

The repository also includes an implementation of the Mixture-of-Experts (MoE) layer, which can be used as a replacement for the standard feed-forward layers in the Transformer model. The MoeLayer class, located in the …/moe.py file, provides this functionality.

Additionally, the repository includes support for Rotary Positional Embeddings, which are used to incorporate positional information into the Transformer model. The precompute_freqs_cis() and apply_rotary_emb() functions, located in the …/rope.py file, handle the precomputation and application of the Rotary Positional Embeddings.

The …/tokenizer.py file defines the Tokenizer class, which is responsible for encoding and decoding text using a pre-trained SentencePiece model, allowing the Transformer model to process natural language input and output.

The deploy directory contains the main entry point for the Mistral AI application, entrypoint.sh, which sets up the environment and runs the main application. This script also handles the optional login to the Hugging Face platform using the HF_TOKEN environment variable.

Overall, this repository provides a comprehensive and flexible implementation of a Transformer-based language model, with support for advanced features like Rotary Positional Embeddings, Mixture-of-Experts layers, and pipeline parallelism. The modular design of the codebase allows for easy customization and integration into various applications and platforms.

Transformer Model Implementation
Revise

References: mistral

• • •
Architecture Diagram for Transformer Model Implementation
Architecture Diagram for Transformer Model Implementation

The core implementation of the Transformer model is contained in the mistral directory. This includes the implementation of the attention mechanism, feed-forward neural network, and normalization layers, as well as the overall Transformer model architecture.

Read more

Attention Mechanism
Revise

References: mistral/model.py

The Attention module implements the attention mechanism used in the Transformer model. It applies rotary embeddings to the query and key tensors and uses the memory_efficient_attention() function from the xformers library to perform the attention computation.

Read more

Feed-Forward Neural Network
Revise

References: mistral/model.py

• • •
Architecture Diagram for Feed-Forward Neural Network
Architecture Diagram for Feed-Forward Neural Network

The FeedForward module implements the feed-forward neural network used in the Transformer model. It applies a series of linear transformations and a SILU activation function to the input tensor.

Read more

Normalization Layers
Revise

References: mistral/model.py

• • •
Architecture Diagram for Normalization Layers
Architecture Diagram for Normalization Layers

The RMSNorm module implements the RMS normalization used in the Transformer model. RMS normalization is a type of layer normalization that computes the root-mean-square (RMS) of the input tensor and applies a learnable scaling factor.

Read more

Transformer Block
Revise

References: mistral/model.py

• • •
Architecture Diagram for Transformer Block
Architecture Diagram for Transformer Block

The TransformerBlock module combines the Attention and FeedForward modules, along with the RMSNorm layers, to create a single Transformer block. This block is a key component of the overall Transformer model implementation.

Read more

Transformer Model
Revise

References: mistral/model.py

The Transformer module is the main entry point for the Transformer model, handling pipeline parallelism and overriding the load_state_dict() method to support loading model parameters in a pipeline-parallel setup.

Read more

Mixture-of-Experts Layer
Revise

References: mistral

• • •
Architecture Diagram for Mixture-of-Experts Layer
Architecture Diagram for Mixture-of-Experts Layer

The Mixture-of-Experts (MoE) Layer is an implementation of the MoE architecture, which can be used as a replacement for the standard feed-forward layers in the Transformer model. The key components of the MoE Layer are:

Read more

MoE Layer Architecture
Revise

References: mistral/moe.py

The MoE Layer Architecture describes the overall design and key components of the Mixture-of-Experts (MoE) layer implemented in the …/moe.py file.

Read more

MoE Layer Implementation
Revise

References: mistral/moe.py

• • •
Architecture Diagram for MoE Layer Implementation
Architecture Diagram for MoE Layer Implementation

The MoeLayer class in …/moe.py is responsible for implementing the core functionality of the Mixture of Experts (MoE) layer. The MoE layer is a type of neural network architecture that consists of a set of expert models, a gating network, and a set of arguments that control the behavior of the layer.

Read more

MoE Layer Configuration
Revise

References: mistral/moe.py

• • •
Architecture Diagram for MoE Layer Configuration
Architecture Diagram for MoE Layer Configuration

The MoeArgs class in …/moe.py defines the configuration options for the Mixture-of-Experts (MoE) layer. This class has two key attributes:

Read more

Rotary Positional Embeddings
Revise

References: mistral

• • •
Architecture Diagram for Rotary Positional Embeddings
Architecture Diagram for Rotary Positional Embeddings

The precompute_freqs_cis() function in …/rope.py precomputes the frequency-based cosine and sine values used in the Rotary Positional Embedding (RoPE) technique. This precomputation is necessary for efficiently applying the RoPE to the input query and key tensors in the Transformer model.

Read more

Precomputation of Rotary Positional Embeddings
Revise

References: mistral/rope.py

• • •
Architecture Diagram for Precomputation of Rotary Positional Embeddings
Architecture Diagram for Precomputation of Rotary Positional Embeddings

The precompute_freqs_cis() function in the …/rope.py file is responsible for precomputing the frequency-based cosine and sine values used in the Rotary Positional Embedding (RoPE) technique.

Read more

Application of Rotary Positional Embeddings
Revise

References: mistral/rope.py

• • •
Architecture Diagram for Application of Rotary Positional Embeddings
Architecture Diagram for Application of Rotary Positional Embeddings

The apply_rotary_emb() function in the …/rope.py file is responsible for applying the Rotary Positional Embedding to the input query (xq) and key (xk) tensors in the Transformer model.

Read more

Integration with Transformer Model
Revise

References: mistral/model.py

• • •
Architecture Diagram for Integration with Transformer Model
Architecture Diagram for Integration with Transformer Model

The Rotary Positional Embedding (RoPE) is integrated into the overall Transformer model architecture in the following way:

Read more

Tokenization and Text Processing
Revise

References: mistral

• • •
Architecture Diagram for Tokenization and Text Processing
Architecture Diagram for Tokenization and Text Processing

The Tokenizer class, defined in the …/tokenizer.py file, is responsible for encoding and decoding text using a pre-trained SentencePiece model. The Tokenizer class provides a convenient interface for working with the SentencePiece model, allowing users to easily encode text into token IDs and decode token IDs back into text.

Read more

Tokenizer Implementation
Revise

• • •
Architecture Diagram for Tokenizer Implementation
Architecture Diagram for Tokenizer Implementation

The Tokenizer class, defined in the …/tokenizer.py file, is responsible for encoding and decoding text using a pre-trained SentencePiece model. The class provides a convenient interface for working with the SentencePiece model, allowing users to easily convert text to token IDs and vice versa.

Read more

Tokenizer Configuration
Revise

• • •
Architecture Diagram for Tokenizer Configuration
Architecture Diagram for Tokenizer Configuration

The Tokenizer class in …/tokenizer.py provides configuration options for working with a pre-trained SentencePiece model. The key configuration options are:

Read more

Tokenizer Integration
Revise

References: mistral/model.py

• • •
Architecture Diagram for Tokenizer Integration
Architecture Diagram for Tokenizer Integration

The Tokenizer class, defined in …/tokenizer.py, is tightly integrated into the overall Transformer model architecture, handling the encoding and decoding of text during model input and output.

Read more

Deployment and Configuration
Revise

References: deploy

• • •
Architecture Diagram for Deployment and Configuration
Architecture Diagram for Deployment and Configuration

The deploy directory contains the main entry point for the Mistral AI application, as well as the necessary setup and configuration files.

Read more

Text Generation and Sampling
Revise

References: mistral-src

• • •
Architecture Diagram for Text Generation and Sampling
Architecture Diagram for Text Generation and Sampling

The main.py file in the mistral-src directory contains the main functionality for text generation and sampling using the Mistral AI model.

Read more

Text Generation
Revise

References: mistral

• • •
Architecture Diagram for Text Generation
Architecture Diagram for Text Generation

The text generation functionality is implemented in the mistral directory. The key components are:

Read more

Interactive Demo
Revise

References: deploy

• • •
Architecture Diagram for Interactive Demo
Architecture Diagram for Interactive Demo

The interactive demo allows users to generate text using the Mistral AI model. The main entry point for the application, including the interactive demo, is located in the deploy directory.

Read more