perf(pipeline): implement auto-partition algorithm

[TXacs]

Auto-Partition in torchtitan

Overview

This PR provides an automatic partitioning method that considers the computation cost of embedding layers.
Thsi method involves calculating the floating-point operations (FLOPs) of the embedding layers and constructing an array that incorporates the FLOPs of both the transformer and embedding layers. Subsequently, a heuristic algorithm is employed to identify a balanced pipeline partition.

Solution Architecture

1. Dynamic Cost Analysis
2. Adaptive Partitioning Algorithm
3. Workload Balancing

Performance

Hardware configuration: 4x RTX 3090 24GB, pipeline parallelism dimension is 4.

llama3 配置对比

hidden size	layers	autopipe TPS	default TPS	Speedup
dim=256	6	31,094	29,549	+5.2%
dim=256	12	21,803	21,923	-0.5%
dim=2048	12	3,348	2,616	+28.0%
dim=4096	12	981	761	+28.9%

deepseekv3(without moe) 配置对比

hidden size	layers	autopipe TPS	default TPS	Speedup
dim=256	6	13,373	13,059	+2.4%
dim=256	12	7,714	6,859	+12.5%
dim=2048	12	4,331	3,810	+13.7%
dim=4096	12	2,888	2,561	+12.8%
dim=4096	16	2,207	2,008	+9.9%
dim=8192	16	4,331	3,935	+10.1%

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[tianyu-l]

Thanks. Is it true that the only "real" deltas are

• autopipe.cpp
• pipeline_parallel.py
• profiler.py

[tianyu-l]

This looks interesting -- how much benefit you'd get from having a c++ implementation, compared with a python one?

[TXacs]

Thanks. Is it true that the only "real" deltas are

• autopipe.cpp
• pipeline_parallel.py
• profiler.py

Yes，actually, profile.py also uses the file from DeepSpeed. It would be even better if TorchTitan could provide a more authoritative FLOPs calculation method in the future, so that we could also adapt it for MoE models.

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[H-Huang]

why is it assumed to be 3x?

[H-Huang]

I guess the FlopsProfiler does not estimate the backward flops?