feat: optional torchembed fused Triton RoPE kernel (~3-4x training speedup)

[py-ai-dev]

Summary

Adds an opt-in use_torchembed_rope: bool = False config flag that routes RoPE application through torchembed's fused Triton kernel instead of the plain-PyTorch apply_rope path. All existing behaviour is unchanged by default.

Benchmark

Hardware: NVIDIA GB10, bfloat16, batch=4, n_heads=32, d_qk=128

seq_len	litgpt (ms)	torchembed (ms)	speedup
512	0.41	0.11	3.73x
1024	0.79	0.21	3.76x
2048	1.56	0.41	3.80x
4096	3.10	0.80	3.88x
8192	6.21	1.55	4.01x

Reproduce with:

pip install torchembed
python benchmarks/bench_torchembed_rope.py

Changes

File	What changed
litgpt/config.py	Add use_torchembed_rope: bool = False alongside existing rope_* fields
litgpt/model.py	Lazy import of torchembed; CausalSelfAttention.__init__ creates one TorchembedRotaryEmbedding per block when enabled; forward uses it for the training path, falls back to apply_rope during KV-cache inference
pyproject.toml	New optional-dependencies.torchembed group (torchembed>=0.3.1)
benchmarks/bench_torchembed_rope.py	Standalone timing script

Usage

from litgpt.config import Config
from litgpt.model import GPT

config = Config.from_name("Llama-3.2-1B", use_torchembed_rope=True)
model = GPT(config)

Install the extra dependency:

pip install "litgpt[torchembed]"
# or
pip install torchembed

Compatibility / Safety

• Default off — existing training runs, checkpoints, and configs are unaffected.
• Graceful fallback — raises a clear ImportError at model construction time if torchembed is not installed; never silently degrades.
• KV-cache inference — automatically falls back to the standard apply_rope path when input_pos is not None, so generation is always correct.
• Guarded flags — raises ValueError with a descriptive message if combined with rope_interleave=True or rope_adjustments (YaRN / Llama3 scaling), which have different cos/sin layouts that the torchembed kernel does not match.
• No new required dependency — torchembed stays in an optional extras group.

Testing

# Smoke-test the forward pass
python -c "
import torch
from litgpt.config import Config
from litgpt.model import GPT

c = Config(vocab_size=256, n_layer=2, n_head=4, n_embd=128,
           use_torchembed_rope=True, block_size=64)
out = GPT(c)(torch.randint(0, 256, (2, 32)))
print('OK', out.shape)
"

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