Lab 05: Sequence / Context Parallel
Annotated code reading lab. Running code is optional.
Sequence / Context Parallel
This lab maps directly to the handbook section. Read the related handbook section first, then use the lab page and starter file to connect the concept to concrete variables, shapes, APIs, and interview-ready explanations.
Sequence / Context Parallel
Read sequence/context parallelism as sharding long-context buffers along the token axis.
Mechanism to keep in mind
- `sequence_shard` reduces per-rank token length.
- `attention` communication is implementation-dependent: gather/scatter-style, all-to-all-style or attention-specific.
- `loss` often needs gathered or reduced results.
Annotated Code Preview
Open starter fileStarter preview
Excerpt from code/lab-05-sequence-context-parallel/sequence_context_parallel.md. The linked starter file is the source of truth.
Distributed training scales beyond one device by partitioning data, model state, or computation across ranks. The key questions are what is replicated, what is sharded, which collective runs on the critical path, and how optimizer semantics stay consistent.What each block is doing
- Setup / contract
- `sequence_shard` reduces per-rank token length.
- Main transition
- `attention` communication is implementation-dependent: gather/scatter-style, all-to-all-style or attention-specific.
- Interview hook
- `loss` often needs gathered or reduced results.
Reading checkpoints
- SP/CP targets long sequence memory.
- It is commonly combined with FSDP/TP.
- Implementation constraints are backend-specific.
What this lab prevents
- SP/CP is not pipeline parallelism.
- Sharding sequence does not remove causal semantics.
How to say it out loud
Read sequence/context parallelism as sharding long-context buffers along the token axis. Then explain the code by naming the state being transformed, the axis or shape that matters, and the tradeoff that would appear in a real system.
Additional intuition
- Use official docs and papers for API behavior and factual claims; use blogs only to improve the mental picture.
- If support matrices, performance behavior or backend choices are version-sensitive, check current docs before repeating them.
- A strong interview answer names the state object, the shape or axis it changes, and the tradeoff it creates.