[BUG] Unable to Use quantization_setting for Customizing MoQ in DeepSpeed Inference

[cyx96]

Describe the bug
Unable to customize MoQ using quantization_setting with DeepSpeed inference.

To Reproduce
Follow the example from the DeepSpeed inference tutorial on datatypes and quantized models.

Below is the full script to reproduce the issue:

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
import deepspeed

# Load T5 model and tokenizer
model_name = "t5-small"  # You can change this to other T5 models like 't5-base' or 't5-large'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Define quantization settings
quantize_groups = 8  # Example setting; adjust as needed
mlp_extra_grouping = True  # Example setting; adjust as needed

# Initialize DeepSpeed inference with quantization
model = deepspeed.init_inference(
    model=model,
    mp_size=1,  # Model parallel size (1 if no model parallelism is used)
    quantization_setting=(quantize_groups, mlp_extra_grouping)
)

# Tokenize input text
input_text = "Translate English to French: Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt")

# Perform inference
outputs = model.generate(**inputs)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Print the result
print("Input:", input_text)
print("Output:", output_text)

Expected behavior
The script should take the input in English and produce the French translation using the T5 model. However, an error is raised:

pydantic_core._pydantic_core.ValidationError: 1 validation error for DeepSpeedInferenceConfig
quantization_setting
  Extra inputs are not permitted [type=extra_forbidden, input_value=(8, True), input_type=tuple]
    For further information visit https://errors.pydantic.dev/2.10/v/extra_forbidden

ds_report output

[2024-12-11 10:01:03,448] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
--------------------------------------------------
DeepSpeed C++/CUDA extension op report
--------------------------------------------------
NOTE: Ops not installed will be just-in-time (JIT) compiled at
      runtime if needed. Op compatibility means that your system
      meet the required dependencies to JIT install the op.
--------------------------------------------------
JIT compiled ops requires ninja
ninja .................. [OKAY]
--------------------------------------------------
op name ................ installed .. compatible
--------------------------------------------------
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  async_io: please install the libaio-dev package with apt
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
async_io ............... [NO] ....... [NO]
fused_adam ............. [NO] ....... [OKAY]
cpu_adam ............... [NO] ....... [OKAY]
cpu_adagrad ............ [NO] ....... [OKAY]
cpu_lion ............... [NO] ....... [OKAY]
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
evoformer_attn ......... [NO] ....... [NO]
 [WARNING]  FP Quantizer is using an untested triton version (3.1.0), only 2.3.(0, 1) and 3.0.0 are known to be compatible with these kernels
fp_quantizer ........... [NO] ....... [NO]
fused_lamb ............. [NO] ....... [OKAY]
fused_lion ............. [NO] ....... [OKAY]
/opt/conda/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `dlvsym'
/opt/conda/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `dlopen'
/opt/conda/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `dlclose'
/opt/conda/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `dlerror'
/opt/conda/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `dlsym'
collect2: error: ld returned 1 exit status
gds .................... [NO] ....... [NO]
transformer_inference .. [NO] ....... [OKAY]
inference_core_ops ..... [NO] ....... [OKAY]
cutlass_ops ............ [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
ragged_device_ops ...... [NO] ....... [OKAY]
ragged_ops ............. [NO] ....... [OKAY]
random_ltd ............. [NO] ....... [OKAY]
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.5
 [WARNING]  using untested triton version (3.1.0), only 1.0.0 is known to be compatible
sparse_attn ............ [NO] ....... [NO]
spatial_inference ...... [NO] ....... [OKAY]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/opt/conda/lib/python3.10/site-packages/torch']
torch version .................... 2.5.1+cu124
deepspeed install path ........... ['/opt/conda/lib/python3.10/site-packages/deepspeed']
deepspeed info ................... 0.16.1, unknown, unknown
torch cuda version ............... 12.4
torch hip version ................ None
nvcc version ..................... 12.4
deepspeed wheel compiled w. ...... torch 2.5, cuda 12.4
shared memory (/dev/shm) size .... 188.94 GB

Screenshots
I will provide the full terminal output running my provided script on my machine:

python3 test_moq.py
[2024-12-11 09:56:28,176] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
[2024-12-11 09:56:30,285] [INFO] [logging.py:128:log_dist] [Rank -1] DeepSpeed info: version=0.16.1, git-hash=unknown, git-branch=unknown
Traceback (most recent call last):
  File "/home/chenyuxu/platform/ml/hhemv2/experiments/precision_test/ds_quant_test/test_moq.py", line 22, in <module>
    model = deepspeed.init_inference(
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/__init__.py", line 362, in init_inference
    ds_inference_config = DeepSpeedInferenceConfig(**config_dict)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/config_utils.py", line 57, in __init__
    super().__init__(**data)
  File "/opt/conda/lib/python3.10/site-packages/pydantic/main.py", line 214, in __init__
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
pydantic_core._pydantic_core.ValidationError: 1 validation error for DeepSpeedInferenceConfig
quantization_setting
  Extra inputs are not permitted [type=extra_forbidden, input_value=(8, True), input_type=tuple]
    For further information visit https://errors.pydantic.dev/2.10/v/extra_forbidden

System info (please complete the following information):

• OS: Debian GNU/Linux 11 (bullseye)
• GPU count and types: 8x L4
• Interconnects (if applicable): Just one machine
• Python version: 3.10.15
• Any other relevant info about your setup: Nothing else for now

[tjruwase]

@sfc-gh-reyazda, any thoughts on this? Thanks

[rlanday]

This code right here (and the method it calls) is the only place quantization_setting is referenced in code, right?

DeepSpeed/deepspeed/inference/engine.py

Lines 93 to 95 in f9e158a

	quantization_setting = None
	self._init_quantization_setting(
	quantization_setting) # todo: update with the new quant config for weight quant

It appears that this parameter is not currently implemented (as of b5d18a6)

[sfc-gh-reyazda]

Hi @rlanday @cyx96

Thanks for mentioning this issue. This part has been modified as part of revision of the inference system. Let me take a look and get back to you on this.
Thanks.
Reza