questuion : how to inference Int8 models (GPT) supported through ZeroQuant technology ?

[xk503775229]

i just use deepspeed ZeroQuant to compress my model ,but i dont known how to use deepspeed to inference it .Is there any discribe about it ?

[xk503775229]

Is there any guide to running inference on compressed models(especially ZeroQuant)?
Any help would be appreciated.

[mayank31398]

@xk503775229 bigscience-workshop/Megatron-DeepSpeed#339
This PR adds support for BLOOM ds-inference with fp16 and int8.
The README is not up-to-date yet. I will work on fixing that.

[xk503775229]

@mayank31398 when i use the BLOOM way to load my checkpoint ,

GPT2 checkpoint type is not supported

I was trying out the compression library for ZeroQuant quantization (for GPT-2 model). While I was able to compress the model, I didn't see any throughput/latency gain from the quantization during inference
Any help would be appreciated.

[mayank31398]

Can you share a code snippet you used for loading GPT?
Also, currently, DS-inference uses fp16 special CUDA kernels for inference which is not the case for int8. int8 CUDA kernels will be released later which are much faster than fp16.

[xk503775229]

Many thanks
The following is my code snippet used for loading GPT.

checkpoint.json:

{"type": "GPT2", "checkpoints": ["/root/DeepSpeedExamples/model_compression/gpt2/output/W8A8/pytorch_model.bin"], "version": 1.0}

[mayank31398]

In general, the code is only supposed to work with Megatron checkpoints.
But there is an exception for BLOOM.
Not sure about the reason.
@jeffra can you comment?
I am not sure, I see the following in the DeepSpeed code:

DeepSpeed/deepspeed/runtime/state_dict_factory.py

Lines 22 to 46 in cf638be

	def get_sd_loader_json(json_file, checkpoint_engine):
	if isinstance(json_file, str):
	with open(json_file) as f:
	data = json.load(f)
	else:
	assert isinstance(json_file, dict)
	data = json_file
	sd_type = data['type']
	ckpt_list = data['checkpoints']
	version = data['version']
	ckpt_type = data.get('parallelization', 'pp')
	mp_size = data.get('mp_size', 0)
	if 'bloom' in sd_type.lower():
	return data
	return SDLoaderFactory.get_sd_loader(ckpt_list,
	checkpoint_engine,
	sd_type,
	version)
	@staticmethod
	def get_sd_loader(ckpt_list, checkpoint_engine, sd_type='Megatron', version=None):
	if sd_type == 'Megatron':
	return MegatronSDLoader(ckpt_list, version, checkpoint_engine)
	else:
	assert False, '{} checkpoint type is not supported'.format(sd_type)

[RezaYazdaniAminabadi]

Hi @xk503775229,

Thanks for the interest in trying Int8 for other models. In general, you should be able to do so, however, one issue here is that you want to use this with loading checkpoint, which is not currently supported for all models. Regarding the Int8 inference, have you tried using the init_inference simply with passing int8 without a checkpoint json (let the model be loaded as it was originally with fp16)?
Thanks,
Reza

[david-macleod]

Can you share a code snippet you used for loading GPT? Also, currently, DS-inference uses fp16 special CUDA kernels for inference which is not the case for int8. int8 CUDA kernels will be released later which are much faster than fp16.

hi, is there a timeline for the release of the int8 CUDA kernels?

[yaozhewei]

Hi This will be released as a part of (MII-Azure) later: https://github.com/microsoft/DeepSpeed-MII

[yaozhewei]

Closed for now. Please re-open it if you need further assistance