Use symmetric quantization in the `quantize` subcommand (#2120)

Packing of asymmetric quantization is broken, all (q)zeros values
of `0` get reset to `1`, resulting in a loss of accuracy. So instead
use symmetric quantization. To be able to distinguish models with
symmetric and asymmetric quantization, a new config tensor `gptq_sym` is
added. If this tensor is not present, we assume `sym=False`.

server/text_generation_server/cli.py

@@ -353,6 +353,7 @@ def quantize(
         upload_to_model_id=upload_to_model_id,
         percdamp=percdamp,
         act_order=act_order,
+        sym=True,
     )
 
 

server/text_generation_server/layers/gptq/__init__.py

@@ -393,11 +393,15 @@ class GPTQWeightsLoader(WeightsLoader):
         )
 
     def _get_gptq_params(self, weights: Weights):
-        try:
+        if weights._has_tensor("gptq_bits") and weights._has_tensor("gptq_groupsize"):
             self.bits = weights.get_tensor("gptq_bits").item()
             self.groupsize = weights.get_tensor("gptq_groupsize").item()
             self.desc_act = False
-            self.sym = False
+            # `server quantize` used asymmetric quantization unconditionally
+            # before the `gptq_sym` setting tensor was added.
+            self.sym = (
+                weights.get_tensor("gptq_sym").item()
+                if weights._has_tensor("gptq_sym")
+                else False
+            )
             self.quant_method = "gptq"
-        except (SafetensorError, RuntimeError) as e:
-            pass

server/text_generation_server/layers/gptq/quantize.py

@@ -871,6 +871,7 @@ def quantize(
     upload_to_model_id: Optional[str],
     percdamp: float,
     act_order: bool,
+    sym: bool,
 ):
     print("loading model")
     config = AutoConfig.from_pretrained(
@@ -946,6 +947,7 @@ def quantize(
         percdamp=percdamp,
         act_order=act_order,
         hooks=hooks,
+        sym=sym,
     )
     print(time.time() - tick)
 
@@ -957,6 +959,7 @@ def quantize(
     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(

server/text_generation_server/utils/weights.py

@@ -146,6 +146,13 @@ class Weights:
         slice_ = f.get_slice(tensor_name)
         return slice_
 
+    def _has_tensor(self, tensor_name: str):
+        try:
+            self.get_filename(tensor_name)
+        except Exception:
+            return False
+        return True
+
     def get_shape(self, tensor_name: str):
         return self._get_slice(tensor_name).get_shape()