skyzh/tiny-llm

Features

• Model Inference and Serving - Implements a complete high-throughput model serving stack optimized for Apple Silicon hardware.
• Model Serving and Inference - Provides a complete runtime for loading model weights and serving LLM inference on specialized hardware.
• Apple Silicon Inference - Provides a specialized inference stack and memory management system optimized for Apple M-series hardware.
• Paged KV Cache Management - Manages key-value caches using fixed-size blocks to eliminate memory fragmentation during token generation.
• Gated Linear Units - Implements gated linear units to control token flow through a gated MLP block.
• Gated Activation Computations - Implements the gated MLP activation pattern using a linear projection gated by an activation function.
• Dequantizing Runtimes - Performs matrix multiplication by converting compressed low-bit weights back to full precision during the compute pass.
• Feature Scale Normalization - Scales feature vectors using root mean square normalization for stable model convergence.
• Generative Text Decoding - Produces sequences of tokens from a prompt using a prefill stage and a decoding loop.
• Generative Text Inference - Implements a system to process natural language prompts and generate intelligent text completions using transformer models.
• Temperature Scaling - Scales the probability distribution of next-token logits to balance predictability and creativity.
• Incremental Text Decoding - Generates tokens sequentially by processing an initial prompt followed by iterative single-token steps.
• Inference Batching - Executes inference for multiple prompts in a single pass to maximize total throughput.
• Inference Optimization Kernels - Implements custom low-level kernels to accelerate the token generation and decoding phases.
• KV Cache Management - Manages key-value pairs for multiple concurrent requests to optimize transformer inference efficiency.
• Inference Engines - Serves as a high-throughput execution engine for pre-trained large language models.
• Continuous Batching Strategies - Employs continuous batching to dynamically insert new requests into active inference batches for higher throughput.
• Paged Batching Managers - Allocates and frees pages from a shared pool as requests enter and exit the system to maximize throughput.
• Model Inference - Combines embedding layers and transformer blocks to process sequences and output next-token probabilities.
• Model Inference Engines - Provides a complete inference engine for loading pretrained models and generating text responses.
• Quantized Linear Layers - Executes linear layers using compressed weights to significantly reduce the memory footprint on the processor.
• Grouped-Query Attention - Shares key and value heads across multiple query heads to reduce memory overhead during inference.
• Paged Cache Attention Kernels - Executes multi-head attention directly on paged key-value caches to minimize memory fragmentation.
• Dequantization Matrix Multiplications - Performs on-the-fly dequantization of compressed weights to reduce memory footprint during matrix multiplication.
• Combined Top-K and Top-P Filtering - Combines top-k and top-p filtering to efficiently sample tokens from output probability distributions.
• Token Embeddings - Converts integer tokens into high-dimensional vectors using a weight matrix and handles reverse mapping back to token space.
• MoE Top-K Selection Kernels - Uses top-k selection kernels to route tokens to the most relevant experts in an MoE architecture.
• Key-Value Cache Reuse - Caches transformer key-value tensors across layers to avoid redundant computations during decoding.
• Paged Key-Value Cache Stores - Stores key-value pairs in non-contiguous memory pages to handle variable-length sequences efficiently.
• Transformer Language Models - Implements causal language model architectures including grouped query attention and mixture of experts routing.
• Transformer Models - Implements core transformer architectures, including attention and MLP layers, to create causal language models.
• Token Embedding Layers - Implements token embedding layers that map discrete token IDs to dense vector representations.
• Inference Batching - Groups multiple incoming requests into a single hardware execution pass to maximize throughput.
• Model Serving - Runs model predictions via matrix manipulation APIs to generate human-readable text responses.
• KV Cache Page Allocators - Assigns physical memory pages from a global pool to individual request caches during token generation.
• Quantized Matrix Multiplication - Performs linear algebra by dequantizing compressed low-bit weights on the fly during matrix multiplication.
• Root Mean Square Normalizations - Provides RMSNorm to standardize activations and stabilize inference.
• Attention Computations - Computes scaled dot product attention to determine relationships between query, key, and value tensors.
• Flash-Attention Implementations - Calculates attention using a tiled softmax algorithm to reduce memory overhead and increase throughput.
• Chunked Prefill Mechanisms - Implements mechanisms to split long prompt processing into smaller segments to prevent memory spikes.
• GPU Kernel Implementations - Provides custom low-level hardware kernels to accelerate tensor processing and math operations.
• Inference Performance Optimization - Implements techniques like continuous batching and paged attention to balance inference speed and throughput.
• Mixture of Experts - Directs tokens to specialized subnetworks using a top-k router and grouped matrix multiplication.
• Causal Masking - Prevents attention mechanisms from accessing future tokens by applying a lower triangular causal mask.
• Sparse Routing Architectures - Implements sparse MoE layers that selectively activate a subset of experts per token.
• Model Performance Optimization - Increases execution speed by utilizing custom low-level kernels for tensor processing.
• Quantized Embedding Lookups - Retrieves token representations from a quantized table to reduce memory footprint.
• Multi-Head Attention Mechanisms - Implements multi-head attention layers that coordinate linear projections for parallel processing.
• Tiled Online Softmax - Calculates attention scores in small blocks using a tiled online softmax to optimize memory usage.
• Rotary Positional Embeddings - Injects sequence order information into query and key vectors using rotation matrices.
• Sampling Controls - Provides parameters for adjusting randomness and creativity during inference, including temperature scaling and nucleus sampling.
• Weight Quantization - Integrates compressed weight containers to replace standard linear layers, reducing the overall memory footprint.
• Transformer Blocks - Constructs architectural blocks consisting of normalization, attention mechanisms, and feed-forward networks with residual connections.
• Weight Dequantization - Performs on-the-fly recovery of original precision from compressed weights during model inference.
• Subspace Mappings - Projects input embeddings into independent subspaces via weight matrices as part of multi-head attention.
• Inference Runtime Metadata Management - Maintains block tables and context lengths to coordinate the request scheduler and attention kernels.
• Batch Request Processing - Handles multiple independent prompt sequences simultaneously using batched masking and positional encoding.
• Small Language Models - Tiny language model for educational purposes.

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