WINT2 Quantization
Weights are compressed offline using the CCQ (Convolutional Coding Quantization) method. The actual stored numerical type of weights is INT8, with 4 weights packed into each INT8 value, equivalent to 2 bits per weight. Activations are not quantized. During inference, weights are dequantized and decoded in real-time to BF16 numerical type, and calculations are performed using BF16 numerical type. - Supported Hardware: GPU - Supported Architecture: MoE architecture
CCQ WINT2 is generally used in resource-constrained and low-threshold scenarios. Taking ERNIE-4.5-300B-A47B as an example, weights are compressed to 89GB, supporting single-card deployment on 141GB H20.
Run WINT2 Inference Service
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \
--port 8180 --engine-worker-queue-port 8181 \
--cache-queue-port 8182 --metrics-port 8182 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 32
By specifying --model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle
, the offline quantized WINT2 model can be automatically downloaded from AIStudio. In the config.json file of this model, there will be WINT2 quantization-related configuration information, so there's no need to set --quantization
when starting the inference service.
Example of quantization configuration in the model's config.json file:
"quantization_config": {
"dense_quant_type": "wint8",
"moe_quant_type": "w4w2",
"quantization": "wint2",
"moe_quant_config": {
"moe_w4_quant_config": {
"quant_type": "wint4",
"quant_granularity": "per_channel",
"quant_start_layer": 0,
"quant_end_layer": 6
},
"moe_w2_quant_config": {
"quant_type": "wint2",
"quant_granularity": "pp_acc",
"quant_group_size": 64,
"quant_start_layer": 7,
"quant_end_layer": 53
}
}
}
- For more deployment tutorials, please refer to get_started;
- For more model descriptions, please refer to Supported Model List.
WINT2 Performance
On the ERNIE-4.5-300B-A47B model, comparison of WINT2 vs WINT4 performance:
Test Set | Dataset Size | WINT4 | WINT2 |
---|---|---|---|
IFEval | 500 | 88.17 | 85.40 |
BBH | 6511 | 94.43 | 92.02 |
DROP | 9536 | 91.17 | 89.97 |