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62 lines
2.4 KiB
Markdown
62 lines
2.4 KiB
Markdown
# WINT2 Quantization
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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.
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- **Supported Hardware**: GPU
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- **Supported Architecture**: MoE architecture
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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.
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## Run WINT2 Inference Service
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```
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python -m fastdeploy.entrypoints.openai.api_server \
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--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \
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--port 8180 --engine-worker-queue-port 8181 \
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--cache-queue-port 8182 --metrics-port 8182 \
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--tensor-parallel-size 1 \
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--max-model-len 32768 \
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--max-num-seqs 32
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```
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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.
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Example of quantization configuration in the model's config.json file:
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```
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"quantization_config": {
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"dense_quant_type": "wint8",
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"moe_quant_type": "w4w2",
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"quantization": "wint2",
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"moe_quant_config": {
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"moe_w4_quant_config": {
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"quant_type": "wint4",
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"quant_granularity": "per_channel",
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"quant_start_layer": 0,
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"quant_end_layer": 6
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},
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"moe_w2_quant_config": {
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"quant_type": "wint2",
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"quant_granularity": "pp_acc",
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"quant_group_size": 64,
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"quant_start_layer": 7,
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"quant_end_layer": 53
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}
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}
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}
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```
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- For more deployment tutorials, please refer to [get_started](../get_started/ernie-4.5.md);
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- For more model descriptions, please refer to [Supported Model List](../supported_models.md).
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## WINT2 Performance
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On the ERNIE-4.5-300B-A47B model, comparison of WINT2 vs WINT4 performance:
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| Test Set | Dataset Size | WINT4 | WINT2 |
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|---------|---------|---------|---------|
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| IFEval |500|88.17 | 85.40 |
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|BBH|6511|94.43|92.02|
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|DROP|9536|91.17|89.97|
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|GSM8K|1319|96.21|95.98|
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|CMath|600|96.50|96.00|
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|CMMLU|11477|89.92|86.22| |