Gptq tokenized dataset

optimum/gptq/quantizer.py

@@ -84,9 +84,9 @@ def __init__(
         Args:
             bits (`int`):
                 The number of bits to quantize to, supported numbers are (2, 3, 4, 8).
-            dataset (`Union[List[str],str]`, defaults to None):
-                The dataset used for quantization. You can provide your own dataset in a list of string or just use the original datasets used
-                in GPTQ paper ['wikitext2','c4','c4-new','ptb','ptb-new'].
+            dataset (`Union[List[str],str,Any]`, defaults to None):
+                The dataset used for quantization. You can provide your own dataset in a list of string or in a list of tokenized data or
+                just use the original datasets used in GPTQ paper ['wikitext2','c4','c4-new','ptb','ptb-new'].
             group_size (int, defaults to 128):
                 The group size to use for quantization. Recommended value is 128 and -1 uses per-column quantization.
             damp_percent (`float`, defaults to `0.1`):
@@ -283,14 +283,14 @@ def _replace_by_quant_layers(self, module: nn.Module, names: List[str], name: st
             self._replace_by_quant_layers(child, names, name + "." + name1 if name != "" else name1)
 
     @torch.no_grad()
-    def quantize_model(self, model: nn.Module, tokenizer: Any):
+    def quantize_model(self, model: nn.Module, tokenizer: Optional[Any] = None):
         """
         Quantizes the model using the dataset
 
         Args:
             model (`nn.Module`):
                 The model to quantize
-            tokenizer (`Any`):
+            tokenizer (Optional[`Any`], defaults to None):
                 The tokenizer to use in order to prepare the dataset. You can pass either:
                     - A custom tokenizer object.
                     - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co.
@@ -341,23 +341,28 @@ def quantize_model(self, model: nn.Module, tokenizer: Any):
         device = get_device(model)
 
         # Step 1: Prepare the data
-        if isinstance(tokenizer, str):
-            try:
-                tokenizer = AutoTokenizer.from_pretrained(tokenizer)
-            except Exception:
+        if isinstance(self.dataset, list) and not isinstance(self.dataset[0], str):
+            logger.info("You are using an already tokenized dataset")
+        else:
+            if isinstance(tokenizer, str):
+                try:
+                    tokenizer = AutoTokenizer.from_pretrained(tokenizer)
+                except Exception:
+                    raise ValueError(
+                        f"""We were not able to get the tokenizer using `AutoTokenizer.from_pretrained`
+                        with the string that you have passed {tokenizer}. If you have a custom tokenizer, you can pass it as input.
+                        For now, we only support quantization for text model. Support for vision, speech and multimodel will come later."""
+                    )
+            if self.dataset is None:
+                raise ValueError("You need to pass `dataset` in order to quantize your model")
+            elif isinstance(self.dataset, str):
+                dataset = get_dataset(self.dataset, tokenizer, seqlen=self.model_seqlen, split="train")
+            elif isinstance(self.dataset, list):
+                dataset = [tokenizer(data, return_tensors="pt") for data in self.dataset]
+            else:
                 raise ValueError(
-                    f"""We were not able to get the tokenizer using `AutoTokenizer.from_pretrained`
-                    with the string that you have passed {tokenizer}. If you have a custom tokenizer, you can pass it as input.
-                    For now, we only support quantization for text model. Support for vision, speech and multimodel will come later."""
+                    "You need to pass a list of string, a list of tokenized data or a string for `dataset`"
                 )
-        if self.dataset is None:
-            raise ValueError("You need to pass `dataset` in order to quantize your model")
-        elif isinstance(self.dataset, str):
-            dataset = get_dataset(self.dataset, tokenizer, seqlen=self.model_seqlen, split="train")
-        elif isinstance(self.dataset, list):
-            dataset = [tokenizer(data, return_tensors="pt") for data in self.dataset]
-        else:
-            raise ValueError("You need to pass a list of string or a string for `dataset`")
 
         dataset = prepare_dataset(dataset, pad_token_id=self.pad_token_id, batch_size=self.batch_size)