ejkernel 0.0.31

Accelerate, Optimize performance with streamlined training and serving options with JAX.

ejKernel: High-Performance JAX Kernels for Deep Learning

"The best optimization is the one you don't have to think about."

ejKernel is a production-grade kernel library for JAX that provides highly optimized implementations of deep learning operations with automatic multi-backend support. The library features a sophisticated configuration management system with autotuning, comprehensive type safety, and seamless execution across GPUs, TPUs, and CPUs.

Table of Contents

• Key Features
• Installation
• Quick Start
• Architecture Overview
• Supported Operations
• Advanced Usage
• Development
• Testing
• Contributing
• Citation
• License

Key Features

Intelligent Kernel Management

• 7-Tier Configuration System: Override → Overlay → Memory Cache → Persistent Cache → Autotune → Heuristics → Error
• Automatic Platform Detection: Seamlessly selects optimal implementation based on hardware
• Priority-Based Registry: Multi-backend support with intelligent fallback mechanisms
• Device Fingerprinting: Hardware-specific configuration caching for optimal performance

State-of-the-Art Operations

• 20+ Deep Learning Operations: Flash Attention v2, Ring Attention, Page Attention, Block Sparse, GLA, Lightning, State Space Models (Mamba), and more
• Memory Efficiency: Custom VJP implementations with O(N) memory complexity for attention
• Distributed Support: Full shard_map integration for model and data parallelism
• Mixed Precision: Comprehensive dtype support with automatic gradient conversion

Production-Ready Infrastructure

• Type Safety: Full jaxtyping annotations with runtime validation via beartype
• Comprehensive Testing: Cross-backend validation, performance benchmarks, integration tests
• Atomic Persistence: Thread-safe configuration storage with automatic optimization
• Profiling Integration: Built-in support for JAX profiling and performance monitoring

Installation

Basic Installation

pip install ejkernel

Platform-Specific Installation

# GPU Support (CUDA/ROCm)
pip install ejkernel[gpu]

# TPU Support
pip install ejkernel[tpu]

# Development Installation
git clone https://github.com/erfanzar/ejkernel.git
cd ejkernel
pip install -e ".[dev]"

Dependencies

• Python 3.11-3.13
• JAX >= 0.8.0
• Triton == 3.4.0 (for GPU)
• jaxtyping >= 0.3.2
• beartype >= 0.22.2

Quick Start

Simple API with Automatic Optimization

import jax.numpy as jnp
from ejkernel.modules import flash_attention

# Basic usage - automatic configuration selection
output = flash_attention(
    query, key, value,
    causal=True,
    dropout_prob=0.1
)

# With advanced features
output = flash_attention(
    query, key, value,
    causal=True,
    sliding_window=128,        # Local attention window
    logits_soft_cap=30.0,      # Gemma-2 style soft capping
    attention_mask=mask,       # Custom attention pattern
)

Custom Configuration

from ejkernel.modules import FlashAttentionConfig
from ejkernel.ops.utils.datacarrier import FwdParams, BwdParams

# Create optimized configuration
config = FlashAttentionConfig(
    fwd_params=FwdParams(
        q_blocksize=256,
        kv_blocksize=256,
        num_warps=8,
        num_stages=2
    ),
    bwd_params=BwdParams(
        q_blocksize=128,
        kv_blocksize=128,
        num_warps=4
    ),
    platform="triton",  # Force specific backend
    backend="gpu"
)

output = flash_attention(query, key, value, cfg=config)

Direct Kernel Registry Access

from ejkernel import kernel_registry, Platform, Backend

# Get specific implementation
kernel = kernel_registry.get(
    algorithm="flash_attention",
    platform=Platform.TRITON,
    backend=Backend.GPU
)

# Direct execution
output = kernel(query, key, value, causal=True)

Distributed Execution

import jax
from jax.sharding import Mesh, PartitionSpec as P
from ejkernel.modules import flash_attention

# Setup mesh for distributed execution
devices = jax.devices()
mesh = Mesh(devices, axis_names=("data", "model"))

# Run distributed attention
output = flash_attention(
    query, key, value,
    causal=True,
    mesh=mesh,
    in_specs=(P("data", None), P("data", None), P("data", None)),
    out_specs=P("data", None)
)

Architecture Overview

System Design

ejKernel employs a sophisticated layered architecture that separates concerns while maintaining high performance:

┌─────────────────────────────────────────────────────┐
│ Public API (modules/)                               │
│ Simple functions with sensible defaults             │
├─────────────────────────────────────────────────────┤
│ Operations Layer (ops/)                             │
│ Configuration management, autotuning, caching       │
├─────────────────────────────────────────────────────┤
│ Kernel Registry (kernels/)                          │
│ Platform routing, signature validation              │
├─────────────────────────────────────────────────────┤
│ Backend Implementations (kernels/\_\*)              │
│ Triton, Pallas, XLA, CUDA kernels                   │
└─────────────────────────────────────────────────────┘

Core Components

Kernel Registry

The registry provides automatic platform-specific kernel selection:

@kernel_registry.register("my_operation", Platform.TRITON, Backend.GPU, priority=100)
def my_operation_gpu(x, y):
    # GPU-optimized implementation
    pass

@kernel_registry.register("my_operation", Platform.XLA, Backend.ANY, priority=50)
def my_operation_fallback(x, y):
    # Universal fallback
    pass

# Automatic selection based on available hardware
impl = kernel_registry.get("my_operation")

Configuration Management

Multi-tier configuration system with intelligent fallback:

class ConfigSelectorChain:
    """
    Selection hierarchy:
    1. Override - Explicit user configuration
    2. Overlay - Temporary context overrides
    3. Memory Cache - In-memory lookup
    4. Persistent Cache - Disk-based storage
    5. Autotune - Performance benchmarking
    6. Heuristics - Intelligent defaults
    7. Error - Clear failure message
    """

Custom VJP System

All performance-critical kernels implement memory-efficient gradients:

@jax.custom_vjp
def kernel_with_custom_grad(inputs):
    return forward(inputs)

def kernel_fwd(inputs):
    output, residuals = forward_with_residuals(inputs)
    return output, residuals

def kernel_bwd(residuals, grad_output):
    return efficient_backward(residuals, grad_output)

kernel_with_custom_grad.defvjp(kernel_fwd, kernel_bwd)

Supported Operations

Attention Mechanisms

Algorithm	Description	Memory	Key Features
Flash Attention v2	Memory-efficient exact attention	O(N)	Causal masking, dropout, sliding windows, soft capping
Ring Attention	Distributed sequence parallelism	O(N/P)	Ultra-long sequences, communication overlap
Page Attention	KV-cache optimized inference	O(N)	Block-wise memory, continuous batching
Block Sparse Attention	Configurable sparse patterns	O(N√N)	Local+global, custom patterns
GLA	Gated Linear Attention	O(N)	Linear complexity, gated updates
Lightning Attention	Layer-dependent decay	O(N)	Exponential moving average
MLA	Multi-head Latent Attention	O(N)	Compressed KV representation
Ragged Page Attention v2	Variable-length paged attention	O(N)	Ragged sequences with page caching
Ragged Page Attention v3	Enhanced ragged page attention	O(N)	Attention sinks support, improved handling
Ragged Decode Attention	Variable-length decoding	O(N)	Efficient batched inference
Kernel Delta Attention	Delta-rule linear attention	O(N)	Linear complexity, delta updates, decay control
Unified Attention	vLLM-style paged attention	O(N)	Segmented 3D decode kernel
Prefill Page Attention	Page attention prefill phase	O(N)	Separate prefill handling
Scaled Dot-Product Attention	Standard attention	O(N²)	Basic reference implementation

Other Operations

Operation	Description	Use Case
Grouped MatMul	Efficient batched matrix operations	Expert models, MoE
Grouped MatMul v2	Enhanced with shard_map support	Distributed expert models
Mean Pooling	Variable-length sequence aggregation	Sentence embeddings
Recurrent	Optimized RNN/LSTM/GRU operations	Sequential modeling
Native Sparse	Block-sparse matrix computations	Sparse attention patterns

State Space Models

Operation	Description	Key Features
State Space v1	Mamba1-style SSM	2D A matrix, separate dt_proj, custom VJP for memory efficiency
State Space v2	Mamba2-style SSM	Per-head scalar A, n_groups for parameter grouping, optional gated RMSNorm

Platform Support Matrix

Operation	Triton (GPU)	Pallas (TPU)	XLA (Universal)
Flash Attention v2	✅	✅	✅
Ring Attention	✅	✅	✅
Page Attention	✅	✅	✅
Block Sparse Attention	✅	✅	✅
Ragged Page Attention v2	✅	✅	✅
Ragged Page Attention v3	✅	✅	✅
Ragged Decode Attention	✅	✅	✅
GLA	✅	-	✅
Lightning Attention	✅	-	✅
MLA	✅	🚧	-
Recurrent	✅	-	✅
Mean Pooling	✅	-	✅
Grouped MatMul	-	✅	✅
Grouped MatMul v2	-	✅	-
Native Sparse Attention	✅	-	✅
Kernel Delta Attention	-	-	✅
Unified Attention	✅	-	✅
Prefill Page Attention	-	✅	✅
State Space v1	-	-	✅
State Space v2	-	-	✅

✅ = Production ready | 🚧 = Under development | - = Not available

Advanced Usage

Page Attention for KV-Cache Inference

from ejkernel.modules import page_attention, PageAttentionConfig

# Configure paged attention for inference
config = PageAttentionConfig(
    platform="auto",
    backend="gpu"
)

output = page_attention(
    query=q,
    key_cache=k_cache,
    value_cache=v_cache,
    block_table=block_table,
    cache_seqlens=cache_seqlens,
    cfg=config
)

Ragged Page Attention for Variable-Length Batches

from ejkernel.modules import ragged_page_attention_v3, RaggedPageAttentionv3Config

# For variable-length sequences with attention sinks
config = RaggedPageAttentionv3Config(
    platform="pallas",
    backend="tpu"
)

output = ragged_page_attention_v3(
    query=q,
    key_pages=k_pages,
    value_pages=v_pages,
    lengths=seq_lengths,
    page_indices=page_indices,
    cfg=config
)

Performance Optimization

# Force autotuning for optimal configuration
import os
os.environ["EJKERNEL_AUTOTUNE_POLICY"] = "autotune"
os.environ["EJKERNEL_LOG_AUTOTUNE"] = "1"

# Enable profiling
os.environ["EJKERNEL_OPS_STAMP"] = "json"  # Detailed metadata
os.environ["EJKERNEL_OPS_RECORD"] = "1"    # Record invocations

Custom Kernel Development

from ejkernel.ops.core import Kernel
from ejkernel.modules.operations.configs import BaseOperationConfig
from dataclasses import dataclass

@dataclass
class MyConfig(BaseOperationConfig):
    param1: int = 128
    param2: float = 0.1

class MyKernel(Kernel[MyConfig, Array]):
    def __init__(self):
        super().__init__(op_id="my_kernel")

    def run(self, x, cfg: MyConfig):
        impl = kernel_registry.get("my_kernel", cfg.platform)
        return impl(x, param1=cfg.param1, param2=cfg.param2)

    def heuristic_cfg(self, inv):
        # Return default configuration
        return MyConfig(param1=256)

    def candidate_cfgs(self, inv):
        # Return autotuning candidates
        return [MyConfig(param1=p) for p in [64, 128, 256]]

Integration with Flax Models

import flax.linen as nn
from ejkernel.modules import flash_attention

class TransformerBlock(nn.Module):
    num_heads: int = 8
    head_dim: int = 64

    @nn.compact
    def __call__(self, x, mask=None):
        # Project to Q, K, V
        q = nn.Dense(self.num_heads * self.head_dim)(x)
        k = nn.Dense(self.num_heads * self.head_dim)(x)
        v = nn.Dense(self.num_heads * self.head_dim)(x)

        # Reshape for attention
        shape = (x.shape[0], x.shape[1], self.num_heads, self.head_dim)
        q, k, v = map(lambda t: t.reshape(shape), (q, k, v))

        # Apply ejKernel Flash Attention
        attn_output = flash_attention(
            q, k, v,
            causal=True,
            attention_mask=mask
        )

        # Project output
        return nn.Dense(x.shape[-1])(attn_output.reshape(x.shape))

Development

Setting Up Development Environment

# Clone repository
git clone https://github.com/erfanzar/ejkernel.git
cd ejkernel

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Code Style

The project uses:

• black for code formatting (line length: 121)
• ruff for linting
• mypy/pyright for type checking
• pre-commit for automated checks

Adding New Kernels

1. Implement the kernel in appropriate backend directory:

# ejkernel/kernels/_triton/my_kernel/_interface.py
@kernel_registry.register("my_kernel", Platform.TRITON, Backend.GPU)
def my_kernel_triton(x, config):
    # Implementation
    pass

1. Create module wrapper:

# ejkernel/modules/operations/my_kernel.py
class MyKernel(Kernel[MyKernelConfig, Array]):
    # Module implementation
    pass

1. Add tests:

# test/kernels/_triton/test_my_kernel.py
class TestMyKernel(unittest.TestCase):
    # Test implementation
    pass

1. Update documentation

Testing

Running Tests

# Run all tests
pytest test/

# Platform-specific tests
pytest test/kernels/_xla/          # XLA implementations
pytest test/kernels/_triton/       # Triton implementations
pytest test/kernels/_pallas/       # Pallas implementations

# Specific test patterns
pytest -k "flash_attention"
pytest --verbose --failfast

# Module operations tests
pytest test/test_module_operations.py

Test Categories

• Unit Tests: Individual component testing
• Integration Tests: End-to-end workflows
• Comparison Tests: Cross-backend consistency
• Performance Tests: Regression detection

Benchmarking

Run benchmarks to compare performance across backends:

# General attention benchmarks
python benchmarks/benchmark_attention.py

# Ragged page attention benchmarks
python benchmarks/benchmark_ragged_page_attn.py

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Priority Areas

• TPU/Pallas implementations for existing algorithms
• CUDA native kernels for maximum performance
• New attention mechanisms from recent papers
• Performance optimizations and kernel fusion
• Documentation and examples

Contribution Process

1. Fork the repository
2. Create a feature branch
3. Implement your changes with tests
4. Ensure all tests pass
5. Submit a pull request

Documentation

Comprehensive documentation available at ejkernel.readthedocs.io

• API Reference: Complete API documentation
• Tutorials: Step-by-step guides
• Architecture: Design documentation
• Benchmarks: Performance analysis

Citation

If you use ejKernel in your research, please cite:

@software{ejkernel2024,
  author = {Erfan Zare Chavoshi},
  title = {ejKernel: High-Performance JAX Kernels for Deep Learning},
  year = {2024},
  url = {https://github.com/erfanzar/ejkernel},
  note = {Production-grade kernel library with multi-backend support}
}

License

ejKernel is licensed under the Apache License 2.0. See LICENSE for details.

Acknowledgments

ejKernel builds upon excellent work from:

• JAX - Composable transformations of Python+NumPy programs
• Triton - GPU kernel programming language
• Pallas - JAX kernel language
• Flash Attention - Memory-efficient attention
• EasyDeL - Parent framework for JAX deep learning

Community

• GitHub Issues: Bug reports and feature requests
• Discussions: Community forum
• Email: [e-mail]

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ejKernel - Production-grade kernels for JAX deep learning