Add dynamic quantization support to gemlite layout

[mobicham]

This PR adds dynamic quantization support to the GemLite layout. The new config param is mode:

• mode="weight_only" - no activation quantization (default).
• mode="dynamic" - uses int8 dynamic quantization for A8W8 and fp8 for A8Wn.

import torch

model_id = "meta-llama/Llama-3.1-8B-Instruct"
from transformers import AutoModelForCausalLM, TorchAoConfig
from torchao.quantization import GemliteUIntXWeightOnlyConfig

quant_gemlite = GemliteUIntXWeightOnlyConfig(
    bit_width=8, group_size=None, mode="dynamic" #A8W8
)
quant_config = TorchAoConfig(
    quant_type=quant_gemlite,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="cuda",
    quantization_config=quant_config,
)

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2327

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

✅ No Failures

As of commit d11f3e2 with merge base d6cfdad ():
💚 Looks good so far! There are no failures yet. 💚

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[jerryzh168]

I think by "static" you meant weight only?