LLM Operator Development and Optimization
osama zaman
LLM Operator Development and Optimization
Overview
This project showcases the development and optimization of an LLM Operator using the FlagGems library with Triton backend for enhanced performance.
Environment Setup
# ============================================================
# CELL 1 — Environment Setup (run once)
# ============================================================
import subprocess
# Install FlagGems from the competition fork
subprocess.run([\
"pip", "install", "-q",\
"git+https://github.com/flagos-ai/FlagGems.git"\
], check=True)
Key Components
Runtime: 1m 11s · GPU T4 x2
Tags: GPU
Language: Python
Code Examples
Code Example 1: Importing Libraries
import torch
import triton
Code Example 2: Implementing a Function
# ============================================================
# CELL 3 — LeakyReLU v4: Pre-dispatch threshold + vectorized
# ============================================================
@triton.jit
def leaky_relu_fwd_kernel(
x_ptr, out_ptr,
negative_slope,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
# function implementation
Testing and Results
# ============================================================
# CELL 6 — v3 tests (replace cosh input for fp16)
# ============================================================
# Running tests for the performance of various operators
results = []
License
This Notebook has been released under the Apache 2.0 open source license.
Screenshots
Conclusion
The project presents a robust framework for benchmarking different GPU operations utilizing the Triton kernel with enhanced optimization techniques.
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Posted May 11, 2026
Development and optimization of an LLM Operator using FlagGems with Triton for better performance.
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