NTT123

This is an optimized implementation of RMSNorm inference kernel using Triton, a Python-based GPU programming library. This implementation is a modified version of the excellent RMSNorm kernel from the Unsloth project.

It has two improvements:

int64 for pointer offset: We use int64 instead of the default int32 to compute the pointer offset value. This change prevents overflow when dealing with large sequence lengths where the offset exceeds the maximum int32 value (2B).
In-place computation: Our kernel writes the result back to the input buffer, eliminating the need for additional memory allocation. This approach halves the memory usage compared to traditional implementations that use a separate output buffer.

import torch
import triton

	"""
	RoPE triton kernel
	"""

	import triton
	import triton.language as tl

	@triton.jit
	def _rope_kernel(
	x_ptr, x_row_stride, x_head_stride,

	"""
	This script converts a Hugging Face LLaMA3 model checkpoint to the original LLaMA3 checkpoint format.

	Usage example:
	python convert_hf_to_llama3.py --hf_model_path "path/to/hf/model" --output_path "path/to/output"
	"""

	import torch
	from transformers import LlamaForCausalLM
	import os

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