DeepSeek-V3/README_WEIGHTS.md

# DeepSeek-V3 Weight File Documentation

## 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.

---

## Weight Structure Overview

The DeepSeek-V3 weight file consists of two main 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.
- **Parameter Count**:
  - Total parameters: **671B**
  - Activation parameters: **36.7B** (including 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.
- **Output Layer**:
  - `model.norm.weight`
  - `lm_head.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.
- **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).

#### 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.
- **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.

---

### 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`.

---

## FP8 Weight Documentation

DeepSeek-V3 natively supports 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:

```json
"quantization_config": {
  "activation_scheme": "dynamic",
  "fmt": "e4m3",
  "quant_method": "fp8",
  "weight_block_size": [128, 128]
}
```

- **Quantization Format**:
  - Format type: `fp8` and `e4m3` (corresponding to `torch.float8_e4m3fn`).
  - Weight block size: `128x128`.
- **Activation Quantization Scheme**:
  - Utilizes 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.
- **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`.

Through dequantization of the FP8 weights, runtime operations enable online quantization at a granularity of `per-token-per-128-channel`.

---