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[Doc][Misc] Comprehensive documentation cleanup and grammatical fixes (#8073)

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What this PR does / why we need it?
This pull request performs a comprehensive cleanup of the vLLM Ascend
documentation. It fixes numerous typos, grammatical errors, and phrasing
issues across community guidelines, developer documents, hardware
tutorials, and feature guides. Key improvements include correcting
hardware names (e.g., Atlas 300I), fixing broken links, cleaning up code
examples (removing duplicate flags and trailing commas), and improving
the clarity of technical explanations. These changes are necessary to
ensure the documentation is professional, accurate, and easy for users
to follow.

Does this PR introduce any user-facing change?
No, this PR contains documentation-only updates.

How was this patch tested?
The changes were manually reviewed for accuracy and grammatical
correctness. No functional code changes were introduced.

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Signed-off-by: herizhen <1270637059@qq.com>
Signed-off-by: herizhen <59841270+herizhen@users.noreply.github.com>
This commit is contained in:
herizhen
2026-04-09 15:37:57 +08:00
committed by GitHub
parent c40a387f63
commit 0d1424d81a
71 changed files with 1295 additions and 1296 deletions
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docs/source/user_guide/feature_guide/Fine_grained_TP.md
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@@ -13,7 +13,7 @@ This capability supports heterogeneous parallelism strategies within a single mo
Fine-Grained Tensor Parallelism delivers two primary performance advantages through targeted weight sharding:
- **Reduced Per-Device Memory Footprint**:
Fine-grained TP shards large weight matrices(如 LM Head, o_proj)across devices, lowering peak memory usage and enabling larger batches or deployment on memory-limited hardware—without quantization.
Fine-grained TP shards large weight matrices(e.g., LM Head, o_proj)across devices, lowering peak memory usage and enabling larger batches or deployment on memory-limited hardware—without quantization.
- **Faster Memory Access in GEMMs**:
In decode-heavy workloads, GEMM performance is often memory-bound. Weight sharding reduces per-device weight fetch volume, cutting DRAM traffic and improving bandwidth efficiency—especially for latency-sensitive layers like LM Head and o_proj.