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[Doc][Misc] Improve readability and fix typos in documentation (#8340)

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### What this PR does / why we need it?

This PR improves the readability of the documentation by fixing typos,
correcting command extensions, and fixing broken links in the Chinese
README.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Documentation changes only.

---------

Signed-off-by: sunshine202600 <sunshine202600@163.com>
This commit is contained in:
sunshine202600
2026-04-17 08:54:38 +08:00
committed by GitHub
parent 8952fddc7e
commit 1dd1de8153
46 changed files with 90 additions and 92 deletions
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4
docs/source/developer_guide/Design_Documents/quantization.md
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@@ -24,7 +24,7 @@ The `embedding` method is generally not implemented for quantization, focusing o
The `create_weights` method is used for weight initialization; the `process_weights_after_loading` method is used for weight post-processing, such as transposition, format conversion, data type conversion, etc.; the `apply` method is used to perform activation quantization and quantized matrix multiplication calculations during the forward process.
We need to implement the `create_weights`, `process_weights_after_loading`, and `apply` methods for different **layers** (**attention**, **mlp**, **moe**).
We need to implement the `create_weights`, `process_weights_after_loading`, and `apply` methods for different **layers** (**attention**, **mlp**, **MoE (Mixture of Experts)**).
**Supplement**: When loading the model, the quantized model's description file **quant_model_description.json** needs to be read. This file describes the quantization configuration and parameters for each part of the model weights, for example:
@@ -107,7 +107,7 @@ vLLM Ascend supports multiple quantization algorithms. The following table provi
| `W8A8_DYNAMIC` | INT8 | INT8 | Per-Channel | Per-Token | Dynamic | Dynamic activation quantization with per-token scaling factor calculation |
| `W4A8_DYNAMIC` | INT4 | INT8 | Per-Group | Per-Token | Dynamic | Supports both direct per-channel quantization to 4-bit and two-step quantization (per-channel to 8-bit then per-group to 4-bit) |
| `W4A4_FLATQUANT_DYNAMIC` | INT4 | INT4 | Per-Channel | Per-Token | Dynamic | Uses FlatQuant for activation distribution smoothing before 4-bit dynamic quantization, with additional matrix multiplications for precision preservation |
| `W8A8_MIX` | INT8 | INT8 | Per-Channel | Per-Tensor/Token | Mixed | PD Colocation Scenario uses dynamic quantization for both P node and D node; PD Disaggregation Scenario uses dynamic quantization for P node and static for D node |
| `W8A8_MIX` | INT8 | INT8 | Per-Channel | Per-Tensor/Token | Mixed | We support two deployment modes: PD Colocation (dynamic quantization for both P and D) and PD Disaggregation (dynamic-quant P and static-quant D) |
**Static vs Dynamic:** Static quantization uses pre-computed scaling factors with better performance, while dynamic quantization computes scaling factors on-the-fly for each token/activation tensor with higher precision.