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Improve DeepSeek-V3 Weight File Documentation for Clarity and Readability

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README_WEIGHTS.md
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## New Fields in `config.json`
- **model_type**: Specifies the model type, which is updated to `deepseek_v3` in this release.
- **num_nextn_predict_layers**: Indicates the number of Multi-Token Prediction (MTP) Modules. The open-sourced V3 weights include **1 MTP Module** .
- **quantization_config**: Describes the configuration for FP8 quantization.
- **model_type**: Specifies the model type, which is now set to `deepseek_v3` in this release.
- **num_nextn_predict_layers**: Defines the number of Multi-Token Prediction (MTP) Modules. The open-sourced V3 weights contain **1 MTP Module**.
- **quantization_config**: Details the configuration for FP8 quantization.
---
## Weight Structure Overview
## Weight File Structure Overview
The DeepSeek-V3 weight file consists of two main components: **Main Model Weights** and **MTP Modules**.
The DeepSeek-V3 weight file is divided into two primary components: **Main Model Weights** and **MTP Modules**.
### 1. Main Model Weights
- **Composition**:
- Input/output embedding layers and a complete set of 61 Transformer hidden layers.
- Includes input/output embedding layers and a full set of 61 Transformer hidden layers.
- **Parameter Count**:
- Total parameters: **671B**
- Activation parameters: **36.7B** (including 0.9B for Embedding and 0.9B for the output Head).
- Activation parameters: **36.7B** (which includes 0.9B for Embedding and 0.9B for the Output Head).
#### Structural Details
- **Embedding Layer**:
- `model.embed_tokens.weight`
- **Transformer Hidden Layers**:
- `model.layers.0` to `model.layers.60`, totaling `num_hidden_layers` layers.
- From `model.layers.0` to `model.layers.60`, which correspond to `num_hidden_layers` layers.
- **Output Layer**:
- `model.norm.weight`
- `lm_head.weight`
@ -33,37 +33,37 @@ The DeepSeek-V3 weight file consists of two main components: **Main Model Weight
### 2. Multi-Token Prediction (MTP) Modules
- **Composition**:
- Additional MTP Modules defined by the `num_nextn_predict_layers` field. In this model, the value is set to 1.
- These modules are determined by the `num_nextn_predict_layers` parameter. In this model, the value is set to 1.
- **Parameter Count**:
- Parameters: **11.5B unique parameters**, excluding the shared 0.9B Embedding and 0.9B output Head).
- Activation parameters: **2.4B** (including the shared 0.9B Embedding and 0.9B output Head).
- Parameters: **11.5B unique parameters** (excluding the shared 0.9B Embedding and 0.9B Output Head).
- Activation parameters: **2.4B** (including the shared 0.9B Embedding and 0.9B Output Head).
#### Structural Details
- **embed_tokens**: **Shares parameters** with the Embedding layer of the Main Model weights.
- **enorm & hnorm**: RMSNorm parameters required for speculative decoding.
- **eh_proj**: Parameters for dimensionality reduction projection on the norm results.
- **embed_tokens**: **Shares parameters** with the Main Model’s Embedding layer.
- **enorm & hnorm**: RMSNorm parameters used for speculative decoding.
- **eh_proj**: Parameters used for dimensionality reduction of the normalized outputs.
- **Additional Transformer Hidden Layer**:
- `model.layers.61.self_attn & mlp` (structure identical to the Main Model hidden layers).
- **shared_head**: **Shares parameters** with the output Head of the Main Model weights.
- `model.layers.61.self_attn & mlp` (these are structured the same as the Main Model hidden layers).
- **shared_head**: **Shares parameters** with the Output Head of the Main Model.
---
### Loading Rules
### Layer Loading Rules
- **Main Model Weights**: Loaded via the `num_hidden_layers` parameter in `config.json`.
- **MTP Modules**: Loaded via the `num_nextn_predict_layers` parameter, with layer IDs appended immediately after the Main Model hidden layers. For example:
- If `num_hidden_layers = 61` and `num_nextn_predict_layers = 1`, the MTP Module's layer ID is `61`.
- **Main Model Weights**: These are loaded according to the `num_hidden_layers` field in `config.json`.
- **MTP Modules**: These are loaded using the `num_nextn_predict_layers` field, with MTP layer IDs appended directly after the Main Model’s hidden layers. For example:
- With `num_hidden_layers = 61` and `num_nextn_predict_layers = 1`, the MTP Module layer ID will be `61`.
---
## FP8 Weight Documentation
DeepSeek-V3 natively supports FP8 weight format with 128x128 block scaling.
DeepSeek-V3 natively supports the FP8 weight format with 128x128 block scaling.
### FP8 Configuration
The FP8 weight file introduces a `quantization_config` field to describe the quantization method. Below is an example configuration:
The FP8 weight file introduces a `quantization_config` field, which defines the quantization method. Below is an example of the configuration:
```json
"quantization_config": {
@ -75,20 +75,18 @@ The FP8 weight file introduces a `quantization_config` field to describe the qua
```
- **Quantization Format**:
- Format type: `fp8` and `e4m3` (corresponding to `torch.float8_e4m3fn`).
- Format type: `fp8` and `e4m3` (aligned with `torch.float8_e4m3fn`).
- Weight block size: `128x128`.
- **Activation Quantization Scheme**:
- Utilizes dynamic activation quantization (`dynamic`).
- Uses dynamic activation quantization (`dynamic`).
### Dequantization Method
The FP8 weight file includes a `weight_scale_inv` field, which stores the dequantization scale for each weight block.
- **Storage Format**: `float32 Tensor`, stored alongside the weight data.
- **Storage Format**: Stored as a `float32 Tensor`, alongside the weight data.
- **Dequantization Formula**:
- If the weight block is not aligned to 128, it is zero-padded to 128 before calculating the scale. After quantization, the padded portion is removed.
- The dequantization process is performed as: `(128x128 weight block) * weight_scale_inv`.
- If a weight block is not aligned to 128, it is zero-padded to 128 before calculating the scale. The padded portion is discarded after quantization.
- Dequantization is performed using the formula: `(128x128 weight block) * weight_scale_inv`.
Through dequantization of the FP8 weights, runtime operations enable online quantization at a granularity of `per-token-per-128-channel`.
---
This dequantization process enables runtime operations to apply online quantization on a per-token, per-128-channel basis.