Activations dynamic quantization and static quantization example

[artificialzjy]

I run the example of Llama3-8B on W8A8(INT8) and W8A8(FP8)，But I get no activations quantization schemes in every_layer's parameter；I only can get the weight_scale and weight_zero_point in every_layer's parameter

[HDCharles]

so W8A8 isn't static. all the int quantization schemes are dynamic.

The only static one is FP8 and our test model shows input activation scales

can you provide a full repro?

[artificialzjy]

recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)
and GPTQModifier(targets="Linear", scheme="W8A8", ignore=["lm_head"]), they all dynamic？can you give a static recipe example，this is the model.safetensors.index.json of scheme="FP8_DYNAMIC''："weight_map": {
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[artificialzjy]

and also i want to know dynamic quantization just save weight's quantization parameters，such as weight_scale and zero_point，but will not save input_activation's quantization parameters，the real quantization part is in inference stage with inference engine such as vllm？If this，the vllm will resolve config.json to quantize input_activation at inference stage？

[dsikka]

Hi @artificialzjy

Dynamic quantization will generate the scales during runtime. So nothing will be saved in the checkpoint. In the case of dynamic activation quantization, the scales for the inputs are generated on the fly during inference time.

Yup you are correct - vLLM reads the config.json and resolves the metadata to generate the scales dynamically during inference time.

E.g

"quantization_config": {
--
"config_groups": {
"group_0": {
"format": "int-quantized",
"input_activations": {
"actorder": null,
"block_structure": null,
"dynamic": true,
"group_size": null,
"num_bits": 8,
"observer": null,
"observer_kwargs": {},
"scale_dtype": null,
"strategy": "token",
"symmetric": true,
"type": "int",
"zp_dtype": null

vLLM will read the value "true" for the field "dynamic" which will inform the generation of the quantization scales for the inputs during inference time.

Does that answer your question?