Skip to content

Instantly share code, notes, and snippets.

@bmorphism

bmorphism/mlx-mcp.md

Created April 1, 2025 13:20
Show Gist options
  • Save bmorphism/17cf157c5a31e4bff687faaa7e9626b4 to your computer and use it in GitHub Desktop.
Save bmorphism/17cf157c5a31e4bff687faaa7e9626b4 to your computer and use it in GitHub Desktop.
Download ZIP
mlx-mcp
Raw
mlx-mcp.md

This session is being continued from a previous conversation that ran out of context. The conversation is summarized below: Let me analyze the conversation chronologically:

  1. Initial Request:

    • The user requested to clone all repositories from the ml-explore organization that had recent code changes, sorted by recency.
    • The repositories should be cloned to the .topos/ml-explore directory.

  2. My Approach to Address the Request:

    • I used GitHub API to search for repositories in the ml-explore organization.
    • I identified 7 repositories that had changes in the last year.
    • I cloned all these repositories to the .topos/ml-explore directory.

  3. Extended Request:

    • The user indicated more repositories were needed.
    • I performed additional GitHub searches for more ml-explore repositories, finding one more (mlx-onnx).
    • I cloned this additional repository.

  4. Further Request:

    • The user asked to search for "mlx-graph" specifically on Exa.
    • I performed an Exa search for mlx-graph repositories.
    • I found and cloned mlx-graphs along with two additional related repositories (NBFNet_mlx and mlx-GCN).

  5. Speech Synthesis Request:

    • The user requested to use say/MCP to vocalize information about the repositories while doing a random walk through the cloned directories.
    • I used the speak feature to verbalize compressed descriptions of repositories during the exploration.
    • The user later asked to change voice to "Rocko Italian" and list available voices.
    • The user had me stop speech with commands like "pkill Emma" and "pkill say".

  6. ASCII Art Request:

    • The user requested ASCII art diagrams to represent different systems after finding a paper about ASCII art diagrams.
    • I created multiple ASCII art diagrams illustrating different aspects of the MLX ecosystem:
      • MLX Ecosystem overall structure
      • Unified memory architecture
      • MLX-Graphs architecture
      • MLX-LM architecture
      • MLX-Swift integration
      • MLX-C API
      • MLX-ONNX architecture
      • MLX-DATA architecture
      • MLX computation flow
      • Graph Neural Network operation

  7. Research Paper:

    • The user shared a research paper about ASCII art diagrams in code.
    • I provided a brief response acknowledging the relevance of the paper to our ASCII diagram creation activities.

  8. Current Work:

    • The user asked me to "practice our hand" - appears to be an interrupted or partial request.
    • Then they requested a detailed summary of the conversation.

Key technical concepts include:

  • MLX framework and ecosystem for machine learning on Apple Silicon
  • Graph Neural Networks (GNNs)
  • Unified memory architecture
  • Swift, C, and Python API bindings
  • ONNX integration
  • Language model processing
  • Message passing in graph networks
  • Lazy computation in MLX
  • ASCII art diagrams for technical documentation

Files and repositories examined include all the ml-explore repositories:

  • mlx
  • mlx-c
  • mlx-data
  • mlx-examples
  • mlx-lm
  • mlx-onnx
  • mlx-swift
  • mlx-swift-examples
  • mlx-graphs
  • Additional related repositories: NBFNet_mlx and mlx-GCN
1. Primary Request and Intent: The user initially requested to "clone all of the repos of ml-explore org that can be found via exa or github based on the coinflip to have had a code change in the last year, by recency first, github confirm the count and clone all such repos under .topos/ml-explore". Later requests expanded the scope to find additional repositories, particularly "mlx-graph" repositories, and then to create ASCII art diagrams representing these systems. The intent was to gather all relevant repositories from the ml-explore organization and create visual representations of their architecture and relationships.

  1. Key Technical Concepts:

    • MLX: Apple's array framework for machine learning on Apple Silicon
    • Unified memory architecture: No copies needed between compute devices
    • Language bindings: Python, C++, C, and Swift APIs for MLX
    • Graph Neural Networks (GNNs): Neural networks operating on graph-structured data
    • Message passing: Core operation in GNNs with message, aggregation, and update phases
    • Lazy computation: MLX's evaluation model where operations are only executed when needed
    • LLM processing: Large language model inference using MLX
    • ONNX: Open Neural Network Exchange format support
    • ASCII art diagrams: Text-based visual representations using monospace characters

  2. Files and Code Sections:

    • All repositories were cloned to /Users/barton/Tritwies/.topos/ml-explore/

    • Main repositories from ml-explore organization:

      • mlx: Core MLX framework for Apple Silicon
      • mlx-c: C API for MLX
      • mlx-data: Framework-agnostic data loading
      • mlx-examples: Examples using MLX
      • mlx-lm: LLM runtime using MLX
      • mlx-onnx: ONNX support for MLX
      • mlx-swift: Swift API for MLX
      • mlx-swift-examples: Examples using MLX Swift

    • Additional related repositories:

      • mlx-graphs: Graph Neural Network library built on MLX
      • NBFNet_mlx: Neural Bellman-Ford networks for Apple Silicon
      • mlx-GCN: Graph Convolutional Network implementation with MLX

    • ASCII art diagrams were created for each major component, including:

      +---------------------------------------------+
|                                             |
|                MLX ECOSYSTEM                |
|                                             |
+---------------------+-----------------------+
                      |

      (Multiple ASCII diagrams created to represent different aspects of the ecosystem)

  3. Problem Solving:

    • Resolved an issue with relative paths during the initial clone operations by using absolute paths
    • Fixed an issue when trying to include Python code in the ASCII art diagram about GNNs
    • Used pkill commands to stop speech synthesis processes when requested
    • Adapted voice selection to use Rocko Italian as requested by the user

  4. Pending Tasks:

    • None explicitly stated at the end of the conversation

  5. Current Work: The most recent substantive work was creating ASCII art diagrams representing the MLX ecosystem and related components. After sharing a research paper titled "Taking ASCII Drawings Seriously: How Programmers Diagram Code," the user made a brief request to "practice our hand" before asking for this summary. The ASCII diagrams created included:

    • MLX Ecosystem overview showing relationships between components
    • Unified memory architecture in Apple Silicon
    • MLX-Graphs architecture and message passing pattern
    • MLX-LM architecture for language model processing
    • MLX-Swift integration with Apple platforms
    • MLX-C API structure
    • MLX-ONNX integration pattern
    • MLX-DATA architecture
    • MLX computation flow showing lazy evaluation
    • Graph Neural Network operation details

  6. Optional Next Step: Since the user's last substantive request was to create ASCII art diagrams of the MLX ecosystem (which was completed), and their immediate last request was for a summary, there is no clear next step to take without further direction from the user. The "practice our hand" request was too vague to determine a specific next action.

. Please continue the conversation from where we left it off without asking the user any further questions. Continue with the last task that you were asked to work on.

⏺ I'll continue where we left off. Let's explore the mlx-examples repository since that's our current working directory.

⏺ List(.)… ⎿  Listed 248 paths (ctrl+r to expand)

⏺ Now, let's take a look at the README.md to understand what this repository contains.

⏺ Read(README.md)… ⎿  Read 77 lines (ctrl+r to expand)

⏺ Let's continue with practicing our hand at visualizing some of these examples. To better understand these examples, let's create an ASCII art diagram for one of the interesting examples in this repository: the Graph Convolutional Network (GCN).

First, let's look at the GCN implementation:

⏺ Read(gcn/gcn.py)… ⎿  Read 32 lines (ctrl+r to expand)

⏺ Read(gcn/main.py)… ⎿  Read 122 lines (ctrl+r to expand)

⏺ Let's create an ASCII art diagram visualizing the GCN architecture in MLX:

                +--------------------------------------------+
                |                    GCN                     |
                +--------------------------------------------+
                                    |
                                    v
                +--------------------------------------------+
                |                 Input Layer                |
                |   [Node Features (x) + Adjacency Matrix]   |
                +--------------------------------------------+
                                    |
                                    v
           +-----------------------------------------------+
           |                  GCN Layers                   |
           |  +----------------------------------------+   |
           |  |              GCN Layer 1               |   |
           |  |  1. Linear Transform: W·x + b          |   |
           |  |  2. Neighborhood Aggregation: adj @ x  |   |
           |  |  3. ReLU Activation                    |   |
           |  |  4. Dropout                            |   |
           |  +----------------------------------------+   |
           |                      |                        |
           |                      v                        |
           |  +----------------------------------------+   |
           |  |              GCN Layer 2               |   |
           |  |  1. Linear Transform: W·x + b          |   |
           |  |  2. Neighborhood Aggregation: adj @ x  |   |
           |  |  3. ReLU Activation                    |   |
           |  |  4. Dropout                            |   |
           |  +----------------------------------------+   |
           |                      |                        |
           |                      v                        |
           |  +----------------------------------------+   |
           |  |             Output Layer               |   |
           |  |  1. Linear Transform: W·x + b          |   |
           |  |  2. Neighborhood Aggregation: adj @ x  |   |
           |  +----------------------------------------+   |
           +-----------------------------------------------+
                                    |
                                    v
                +--------------------------------------------+
                |                   Loss                     |
                |        Cross Entropy + L2 Regularization   |
                +--------------------------------------------+
                                    |
                                    v
                +--------------------------------------------+
                |                Optimizer                   |
                |                   Adam                     |
                +--------------------------------------------+

This ASCII art diagram shows:

  1. Input layer with node features (x) and adjacency matrix
  2. Multiple GCN layers, each with: - Linear transformation - Neighborhood aggregation (matrix multiplication with adjacency matrix) - ReLU activation (except the output layer) - Dropout (except the output layer)
  3. Loss calculation with cross-entropy and optional L2 regularization
  4. Adam optimizer for parameter updates

Now, let's look at one more intriguing example - the Stable Diffusion implementation:

⏺ Read(stable_diffusion/txt2image.py)… ⎿  Read 109 lines (ctrl+r to expand)

⏺ Let's create an ASCII art diagram for the Stable Diffusion image generation pipeline in MLX:

+------------------------------------------------------+ | Stable Diffusion in MLX | +------------------------------------------------------+ | v +------------------------------------------------------+ | Text Input | | "a photograph of an astronaut | | riding a horse on mars" | +------------------------------------------------------+ | v +--------------------------------------------------+ | Text Encoders | | +-------------------------------------------+ | | | CLIP Text Encoder (T5/BERT) | | | | Converts text prompt to embedding vector | | | +-------------------------------------------+ | +--------------------------------------------------+ | v +--------------------------------------------------+ | Latent Generation | | +-------------------------------------------+ | | | Diffusion Process | | | | | | | | 1. Start with random noise (x_T) | | | | 2. Iterative denoising steps: | | | | For t = T, T-1, ..., 1: | | | | - Predict noise using UNet | | | | - Apply classifier-free guidance | | | | - Update latent x_t → x_(t-1) | | | | | | | +-------------------------------------------+ | +--------------------------------------------------+ | v +--------------------------------------------------+ | VAE Decoder | | +-------------------------------------------+ | | | Converts latents to pixel space | | | | (scaling, denormalization) | | | +-------------------------------------------+ | +--------------------------------------------------+ | v +--------------------------------------------------+ | Post-processing | | +-------------------------------------------+ | | | - Grid arrangement of multiple images | | | | - Padding and reshaping | | | | - Convert to uint8 for saving | | | +-------------------------------------------+ | +--------------------------------------------------+ | v +--------------------------------------------------+ | Final Image | | saved to disk | +--------------------------------------------------+

This ASCII art diagram shows:

  1. Text input (the prompt)
  2. Text encoding using CLIP text encoder
  3. Latent generation through the diffusion process: - Starting with random noise - Iterative denoising with UNet and classifier-free guidance
  4. VAE decoding to convert latents to pixel space
  5. Post-processing for creating image grids
  6. Final image saved to disk

These examples demonstrate the breadth of machine learning tasks that MLX supports on Apple Silicon, from graph neural networks to state-of-the-art diffusion models, all implemented with a clean, PyTorch-like API.

perfect the technique

⏺ Read(whisper/mlx_whisper/whisper.py)… ⎿  Read 267 lines (ctrl+r to expand)

⏺ +--------------------------------------------------------------+ | MLX Whisper Model | +--------------------------------------------------------------+ | +----------------+----------------+ | | +-----------------+ +-----------------+ | Audio Encoder | | Text Decoder | +-----------------+ +-----------------+ | | v v +----------------------------------+ +----------------------------------+ | Mel Spectrogram Input | | Tokenized Text Input | | | | | | +----------------------------+ | | +----------------------------+ | | | Conv1d Layers | | | | Token Embedding | | | | - 2 layers w/ GELU | | | | Position Embedding | | | +----------------------------+ | | +----------------------------+ | | | | | | | | v | | v | | +----------------------------+ | | +----------------------------+ | | | Positional Embedding | | | | Self-Attention Blocks | | | +----------------------------+ | | | with Cross-Attention | | | | | | | - n_text_layer blocks | | | v | | | - Multi-head attention | | | +----------------------------+ | | | - Layer normalization | | | | Self-Attention Blocks | | | | - Residual connections | | | | - n_audio_layer blocks | | | | - MLP with GELU | | | | - Multi-head attention |──+──+──> - causal masking | | | | - Layer normalization | | | +----------------------------+ | | | - Residual connections | | | | | | | - MLP with GELU | | | v | | +----------------------------+ | | +----------------------------+ | | | | | | Final Layer Norm | | | v | | +----------------------------+ | | +----------------------------+ | | | | | | Final Layer Norm | | | v | | +----------------------------+ | | +----------------------------+ | +----------------------------------+ | | Linear Projection | | | | (to vocabulary) | | | +----------------------------+ | +----------------------------------+ | v +----------------------------------+ | Decoding Functions | | +----------------------------+ | | | Language Detection | | | +----------------------------+ | | +----------------------------+ | | | Transcription Decoding | | | +----------------------------+ | +----------------------------------+

Here's an ASCII art diagram for the MLX implementation of Stable Diffusion's UNet architecture:

⏺ Read(stable_diffusion/stable_diffusion/unet.py)… ⎿  Read 50 lines (ctrl+r to expand)

⏺ +-------------------------------------------------------------+ | Stable Diffusion UNet | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | Input Processing | | +-------------------------------------------------------+ | | | Timestep Embedding | | | | - SILU activation | | | | - Sequence of linear projections | | | +-------------------------------------------------------+ | | | | | +-------------------------------------------------------+ | | | Conditional Embedding (Text/Image) | | | | - CLIP text encoding or image encoding | | | +-------------------------------------------------------+ | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | Encoder Pathway | | +-------------------------------------------------------+ | | | DownBlocks (Resolution Reduction) | | | | - ResNet blocks | | | | - Cross-Attention with conditional embeddings | | | | - Self-Attention | | | | - Spatial Downsampling | | | +-------------------------------------------------------+ | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | Middle Block | | +-------------------------------------------------------+ | | | - ResNet blocks | | | | - Cross-Attention with conditional embeddings | | | | - Self-Attention | | | +-------------------------------------------------------+ | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | Decoder Pathway | | +-------------------------------------------------------+ | | | UpBlocks (Resolution Restoration) | | | | - ResNet blocks | | | | - Cross-Attention with conditional embeddings | | | | - Self-Attention | | | | - Skip connections from encoder | | | | - Spatial Upsampling | | | +-------------------------------------------------------+ | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | Output Processing | | +-------------------------------------------------------+ | | | - Group normalization | | | | - SILU activation | | | | - Conv2d projection to output channels | | | +-------------------------------------------------------+ | +-------------------------------------------------------------+ | v +-------------------------------------------------------------+ | Noise Prediction Output | | (Used for iterative denoising process) | +-------------------------------------------------------------+

Now let's create a detailed ASCII art diagram for the MLX computation flow pattern, highlighting its lazy evaluation model:

+-----------------------------------------------------------------------+ | MLX Computation Flow | +-----------------------------------------------------------------------+ | v +-----------------------------------------------------------------------+ | Computational Graph Building | | | | +-------------------------------------------------------------+ | | | | | | | +--------------------+ +---------------------+ | | | | | Array Creation | | Array Operations | | | | | | mx.array() |--------->| +, -, *, /, @, etc | | | | | | mx.zeros() | | | | | | | | mx.ones() | | | | | | | +--------------------+ +---------------------+ | | | | | | | | | v | | | | +--------------------+ +---------------------+ | | | | | Array Functions |<-------->| Array Transformations| | | | | | mx.sin() | | reshape, transpose | | | | | | mx.exp() | | concatenate, etc | | | | | | mx.log() | | | | | | | +--------------------+ +---------------------+ | | | | | | | | Operations are tracked but not executed | | | +-------------------------------------------------------------+ | | | +-----------------------------------------------------------------------+ | v +-----------------------------------------------------------------------+ | Lazy Execution Trigger | | | | +-------------------------------------------------------------+ | | | | | | | +-----------------------+ +----------------------+ | | | | | Explicit Evaluation | | Implicit Triggers | | | | | | mx.eval(array) | | - Printing values | | | | | | array.item() | | - Converting to NumPy| | | | | | | | - Saving to disk | | | | | +-----------------------+ +----------------------+ | | | | | | | | | | +------------------------------+ | | | | | | | | +-------------------------------------------------------------+ | | | +-----------------------------------------------------------------------+ | v +-----------------------------------------------------------------------+ | Optimized Execution | | | | +-------------------------------------------------------------+ | | | | | | | +----------------------+ +----------------------+ | | | | | Graph Optimization |------->| Memory Planning | | | | | | - Fusion | | - Buffer allocation | | | | | | - Dead code removal | | - Memory reuse | | | | | | - Common subexpr | | | | | | | +----------------------+ +----------------------+ | | | | | | | | | | v v | | | | +----------------------+ +----------------------+ | | | | | Device Selection |------->| Kernel Execution | | | | | | - CPU | | - Primitives | | | | | | - Metal (GPU) | | - BLAS/FFT/etc | | | | | | - Unified memory | | - Custom kernels | | | | | +----------------------+ +----------------------+ | | | | | | | +-------------------------------------------------------------+ | | | +-----------------------------------------------------------------------+ | v +-----------------------------------------------------------------------+ | Results Materialization | | | | +-------------------------------------------------------------+ | | | | | | | +----------------------+ +----------------------+ | | | | | Output Arrays | | Result Caching | | | | | | - Populated values |------->| - Reuse computations | | | | | | - Correct shape | | - Avoid redundant | | | | | | - Correct type | | calculations | | | | | +----------------------+ +----------------------+ | | | | | | | +-------------------------------------------------------------+ | | | +-----------------------------------------------------------------------+

look deeper

⏺ Let's look deeper into the MLX framework by examining its core components and advanced features. First, let's examine the MLX core implementation:

⏺ Task(Find the main implementation files for MLX core in the ml-explore/mlx repository. Look for files that define the core array operations, primitive functions, and device … abstractions. Focus on key C++ files and Python bindings. Summarize the overall architecture of MLX core based on these files.) ⎿  Done (9 tool uses · 25.2k tokens · 30.6s)

⏺ Let's explore specific implementation details in the native execution layer. Let's start by examining the Metal implementation in MLX:

⏺ Search(pattern: "/metal//*.{h,cpp,mm}", path: "..")… ⎿  Found 100 files (ctrl+r to expand)

⏺ Let's look at the core Metal implementation to understand how MLX leverages Apple's GPU architecture:

⏺ Read(../mlx/mlx/backend/metal/metal.h)… ⎿  Read 24 lines (ctrl+r to expand)

⏺ Read(../mlx/mlx/backend/metal/device.h)… ⎿  Read 282 lines (ctrl+r to expand)

⏺ Let's also look at one of the core kernel implementations:

⏺ Read(../mlx/mlx/backend/metal/kernels/steel/gemm/gemm.h)… ⎿  Read 295 lines (ctrl+r to expand)

⏺ Now let's look at the automatic differentiation implementation:

⏺ Search(pattern: "**/grad*.{h,cpp}", path: "..")… ⎿  Found 0 files (ctrl+r to expand)

⏺ Search(pattern: "class Grad", path: "..")… ⎿  Found 0 files (ctrl+r to expand)

⏺ Search(pattern: "autograd", path: "..")… ⎿  Found 3 files (ctrl+r to expand)

⏺ Let's look at one of these autograd tests to understand the implementation structure:

⏺ Read(../mlx/python/tests/test_autograd.py)… ⎿  Read 803 lines (ctrl+r to expand)

⏺ Now let's explore the core architecture in more detail by creating another diagram that illustrates MLX's advanced lazy evaluation, automatic differentiation, and execution model:

+-------------------------------------------------------+ | MLX Core Architecture | +-------------------------------------------------------+ | v +-------------------------------------------------------+ | Array Abstraction | | +------------------+ +-------------------+ | | | Array Class | | Array Operations | | | | - dtype | | - Primitive ops | | | | - shape | | - Math functions | | | | - data |------->| - Array funcs | | | | - device | | - Indexing | | | | - lazy flags | | - Transformations | | | +------------------+ +-------------------+ | +-------------------------------------------------------+ | v +-------------------------------------------------------+ | Computational Graph | | +------------------+ +------------------+ | | | Operation Node | | Dependency | | | | - input arrays |<------>| Tracking | | | | - operation type | | - inputs->outputs| | | | - attributes | | - outputs->inputs| | | | - output shape | | | | | +------------------+ +------------------+ | +-------------------------------------------------------+ | v +-------------------------------------------------------+ | Automatic Differentiation | | +------------------+ +------------------+ | | | Forward Mode | | Reverse Mode | | | | - JVP (Jacobian | | - VJP (Vector | | | | Vector Product)| | Jacobian Prod.)| | | | - Tangent calc | | - Cotangent calc | | | | - Function comp. | | - Grad function | | | +------------------+ +------------------+ | | | | | | v v | | +------------------+ +------------------+ | | | Custom Gradients | | Checkpointing | | | | - override vjp | | - Memory-compute | | | | - override jvp | | tradeoff | | | | - special funcs | | - Rematerialization | | +------------------+ +------------------+ | +-------------------------------------------------------+ | v +-------------------------------------------------------+ | Device-Specific Backends | | +------------------+ +------------------+ | | | CPU Backend | | Metal Backend | | | | - BLAS | | - Steel Kernels | | | | - LAPACK | | - GEMM | | | | - FFT | | - Attention | | | | - Custom kernels | | - Convolution | | | +------------------+ +------------------+ | | | | +------------------+ +------------------+ | | | Memory Management| | Kernel Compilation| | | | - Pooling | | - JIT | | | | - Allocation | | - Metal shaders | | | | - Unified memory | | - Specialization | | | +------------------+ +------------------+ | +-------------------------------------------------------+ | v +-------------------------------------------------------+ | Higher-level Libraries | | +------------------+ +------------------+ | | | Neural Networks | | Optimizers | | | | - Layers | | - SGD | | | | - Activations | | - Adam | | | | - Loss functions | | - LARS | | | | - Initializers | | - Learning rates | | | +------------------+ +------------------+ | | | | +------------------+ +------------------+ | | | Model Definitions| | Training Utils | | | | - Sequential | | - Metrics | | | | - Module | | - Schedules | | | | - Functional | | - Checkpointing | | | +------------------+ +------------------+ | +-------------------------------------------------------+

This detailed architectural diagram shows:

  1. Array Abstraction: The core data structure that manages data types, shapes, and device placement.
  2. Computational Graph: Representation of operations and their dependencies for lazy evaluation.
  3. Automatic Differentiation: Both forward and reverse mode differentiation for training neural networks.
  4. Device-Specific Backends: Specialized implementations for CPU and Metal (Apple Silicon GPU) using optimized kernels.
  5. Higher-level Libraries: Neural network building blocks and optimization algorithms built on top of the core.

MLX's key innovations include:

  1. Unified Memory Architecture: Data remains in the same memory space regardless of which compute device (CPU or GPU) is used, avoiding costly data transfers.
  2. Lazy Evaluation: Operations are recorded but not executed until explicitly required, allowing for optimization.
  3. JIT Compilation: Just-in-time compilation of operation graphs for efficient execution.
  4. Specialized Metal Kernels: Hand-optimized kernels (like "Steel") for Apple Silicon GPUs.
  5. Python-first API: Designed to be familiar to users of other ML frameworks while leveraging Apple's hardware.

These components work together to provide a high-performance, flexible machine learning framework optimized for Apple Silicon hardware. The architecture enables efficient model definition, training, and inference with minimal overhead, making it suitable for both research and production use cases.

LOOK DEEPER

⏺ Let's dive even deeper into the MLX architecture by examining the critical components and internal structures that make it unique. First, let's look at the steel kernel implementation for matrix multiplication (one of the most crucial operations for deep learning):

⏺ Read(../mlx/mlx/backend/metal/kernels/steel/gemm/mma.h)… ⎿  Read 655 lines (ctrl+r to expand)

⏺ Now let's look at the core array implementation to understand the foundation of MLX:

⏺ Search(pattern: "class Array", path: "../mlx/mlx")… ⎿  Found 1 file (ctrl+r to expand)

⏺ Read(../mlx/mlx/array.h)… ⎿  Read 250 lines (ctrl+r to expand)

⏺ Now, let's explore the lazy evaluation and execution model implementation:

⏺ Search(pattern: "class Primitive", path: "../mlx/mlx")… ⎿  Found 2 files (ctrl+r to expand)

⏺ Read(../mlx/mlx/primitives.h)… ⎿  Read 150 lines (ctrl+r to expand)

⏺ Let's create a comprehensive ASCII art diagram that shows the deep internal architecture of MLX, focusing on the unique aspects revealed by examining the core code:

+------------------------------------------------------------------------------+ | MLX DEEP ARCHITECTURE | +------------------------------------------------------------------------------+ | +--------------------------|---------------------------+ | | | +------------------+ +----------------------+ +------------------+ | Array System | | Execution System | | AD System | +------------------+ +----------------------+ +------------------+

+------------------------------------------------------------------------------+ | ARRAY SYSTEM INTERNALS | +------------------------------------------------------------------------------+ | +----------------+ | +----------------+ | array class |<--|-->| ArrayDesc | +----------------+ | +----------------+ | | | v | v +-------------------------+ | +------------------------+ | - shape, dtype, strides | | | - shared_ptr | | - flags | | | - shared_ptr| | - id() | | | - size | | - contiguous flags | | | - inputs | | - lazy eval markers | | | - siblings | +-------------------------+ | +------------------------+ | v +-------------------------------+ | Memory System | +-------------------------------+ | - Unified memory architecture | | - Zero-copy between devices | | - Custom allocators | | - Buffer management | | - Non-owning weak references | +-------------------------------+

+------------------------------------------------------------------------------+ | EXECUTION SYSTEM INTERNALS | +------------------------------------------------------------------------------+ | +----------------+ | +------------------+ | Primitive class|<--|-->| Operation Graph | +----------------+ | +------------------+ | | | v | v +-------------------------+ | +------------------------+ | - eval_cpu() | | | - Topological ordering | | - eval_gpu() | | | - Dependency tracking | | - output_shapes() | | | - Primitive fusion | | - is_equivalent() | | | - Dead code elimination| | - stream_ | | | - Memory planning | +-------------------------+ | +------------------------+ | | | v v v +-----------------+ +------------------+ +-------------------+ | UnaryPrimitive | | BinaryPrimitive | | Compiled Execution| +-----------------+ +------------------+ +-------------------+ | - Single output | | - Two outputs | | - JIT compilation | | - Vectorizable | | - Vectorizable | | - Metal shaders | +-----------------+ +------------------+ | - Kernel fusion | | - Memory reuse | +-------------------+

+------------------------------------------------------------------------------+ | METAL BACKEND IMPLEMENTATION | +------------------------------------------------------------------------------+ | +-----------------+ | +------------------+ | Metal Device |<--|-->| Command Encoders | +-----------------+ | +------------------+ | | | v | v +-------------------------+ | +------------------------+ | - mtl_device() | | | - setComputePipeline | | - register_library() | | | - setBuffer | | - get_kernel() | | | - dispatch_threads | | - build_library_() | | | - fence management | +-------------------------+ | +------------------------+ | | v v +--------------------+ +------------------------+ | Steel Kernels | | Memory Management | +--------------------+ +------------------------+ | - MMAFrag | | - Residency tracking | | - GEMMKernel | | - Command buffering | | - BlockMMA | | - Barrier insertion | | - tile_matmad | | - Resource tracking | | - SIMD optimized | | - Concurrent contexts | +--------------------+ +------------------------+

+------------------------------------------------------------------------------+ | AUTOMATIC DIFFERENTIATION ENGINE | +------------------------------------------------------------------------------+ | +-----------------+ | +------------------+ | Primitive::jvp |<--|-->| Primitive::vjp | +-----------------+ | +------------------+ | | | v | v +-------------------------+ | +------------------------+ | - Forward-mode AD | | | - Reverse-mode AD | | - Tangent propagation | | | - Cotangent propagation| | - Jacobian calculation | | | - Gradient accumulation| +-------------------------+ | +------------------------+ | | v v +--------------------+ +------------------------+ | Custom Derivatives | | Vectorized Primitives | +--------------------+ +------------------------+ | - custom_function | | - vmap implementation | | - override jvp/vjp | | - Batching rules | | - transform rules | | - Broadcasting | | - checkpointing | | - Parallelization | +--------------------+ +------------------------+

+------------------------------------------------------------------------------+ | LAZY EVALUATION FLOW | +------------------------------------------------------------------------------+ | +-----------------+ | +------------------+ | Array Creation |<--|-->| Graph Building | +-----------------+ | +------------------+ | | | v | v +-------------------------+ | +-------------------------+ | 1. Create ArrayDesc | | | 4. Use primitive to | | 2. Set shape, dtype |--+--| generate output shapes | | 3. Attach primitive | | | 5. Add to graph | +-------------------------+ | +-------------------------+ | | | v v v +----------------+ +----------------+ +----------------+ | Trigger Points | | Eval Process | | Optimization | +----------------+ +----------------+ +----------------+ | - eval() call | | 1. Build graph | | - Fusion | | - item() call | | 2. Topo sort | | - CSE | | - to_numpy() |-->| 3. Allocate mem |-->| - DCE | | - print | | 4. Execute ops | | - Buffer reuse| | - save | | 5. Update state | | - Compilation | +----------------+ +----------------+ +----------------+

This deep architecture diagram highlights several important aspects of MLX:

  1. Array System: - Arrays are views into data with shared ownership through ArrayDesc - Unified memory model allows zero-copy between CPU and GPU - Sophisticated flags track array properties for optimization
  2. Execution System: - Primitives define abstract operations that work on both CPU and GPU - Operation graph builds lazily until evaluation is triggered - Steel kernels provide highly optimized Metal implementations
  3. Metal Backend: - Custom Metal kernels for matrix multiplication, convolution, etc. - SIMD-optimized code targeting Apple's Metal Performance Shaders - Memory management with barriers, fences, and residency tracking
  4. Automatic Differentiation: - First-class support for both forward (JVP) and reverse (VJP) mode - Custom derivative overrides for special functions - Vectorization for batched operations
  5. Lazy Evaluation: - Operations recorded but not executed until needed - Triggers include explicit eval(), item(), printing, saving - Optimizations applied during graph compilation

The core innovation in MLX is its combination of:

  1. The unified memory architecture for Apple Silicon
  2. Lazy evaluation for operation fusion and optimization
  3. First-class automatic differentiation
  4. Native Metal backend with hand-tuned kernels
  5. Python-friendly API that mimics familiar frameworks

This enables high-performance machine learning on Apple devices with a clean, intuitive interface resembling PyTorch but optimized for the Metal architecture.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment