server quantize: store quantizer config in standard format (#2299)

- Create `quantization_config` option in the model config.
- Don't store the quantizer config in tensors anymore.

server/text_generation_server/layers/gptq/quantize.py

@@ -17,7 +17,7 @@ from loguru import logger
 from typing import Optional
 from text_generation_server.layers.gptq.utils import torch_snr_error
 
-from text_generation_server.utils.weights import DefaultWeightsLoader
+from text_generation_server.utils.weights import DefaultWeightsLoader, UnquantizedWeight
 
 DEV = torch.device("cuda:0")
 
@@ -897,7 +897,7 @@ def quantize(
         dtype=torch.float16,
         process_group=process_group,
         aliases={"embed_tokens.weight": ["lm_head.weight"]},
-        weights_loader=DefaultWeightsLoader(),
+        weights_loader=DefaultWeightsLoader(UnquantizedWeight),
     )
     hooks = []
     for name, module in model.named_modules():
@@ -960,9 +960,6 @@ def quantize(
 
     state_dict = model.state_dict()
     state_dict = {k: v.cpu().contiguous() for k, v in state_dict.items()}
-    state_dict["gptq_bits"] = torch.LongTensor([bits])
-    state_dict["gptq_groupsize"] = torch.LongTensor([groupsize])
-    state_dict["gptq_sym"] = torch.BoolTensor([sym])
 
     max_shard_size = "10GB"
     shards, index = shard_checkpoint(
@@ -994,6 +991,15 @@ def quantize(
             f"index located at {save_index_file}."
         )
     config = AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
+    config.quantization_config = {
+        "bits": bits,
+        "group_size": groupsize,
+        "damp_percent": percdamp,
+        "desc_act": act_order,
+        "static_groups": False,
+        "sym": sym,
+        "quant_method": "gptq",
+    }
     config.save_pretrained(output_dir)
     logger.info("Saved config")
     logger.info("Saving tokenizer")