removing 8da4w-gptq

examples/models/llama/source_transformation/quantize.py

@@ -57,7 +57,7 @@ def quantize(  # noqa C901
 
     Args:
         model: The model to quantize.
-        qmode: The quantization mode, e.g. int8, 8da4w, 8da4w-gptq.
+        qmode: The quantization mode, e.g. int8, 8da4w.
         computation_dtype: The dtype that ops are performed in (the resulting dtype of dequantization).
             Also the dtype of the rest of the non-quantized compoents of the model.
         checkpoint_dtype: The dtype of the checkpoint, this arg exists since it is more accurate to
@@ -161,58 +161,6 @@ def quantize(  # noqa C901
         if verbose:
             print("quantized model:", model)
         return model
-    elif qmode == "8da4w-gptq":
-        # Check for required args
-        required_args: Optional[Any] = [
-            group_size,
-            calibration_limit,
-            calibration_seq_length,
-        ]
-        if any(arg is None for arg in required_args):
-            raise Exception(
-                "For 8da4w-gptq quantization, group size, calibration limit and calibration sequence length must be specified."
-            )
-        if calibration_tasks is None:
-            calibration_tasks = ["wikitext"]
-
-        try:
-            # torchao 0.3+
-            from torchao._models._eval import InputRecorder
-        except ImportError:
-            from torchao.quantization.GPTQ import InputRecorder  # pyre-ignore
-
-        from torchao.quantization.quant_api import Int8DynActInt4WeightGPTQQuantizer
-
-        if tokenizer_path is None:
-            assert checkpoint_path is not None, "checkpoint_path must be specified"
-            tokenizer_path = checkpoint_path.parent / "tokenizer.model"
-        assert tokenizer_path.is_file(), tokenizer_path
-        tokenizer = SentencePieceProcessor(  # pyre-ignore[28]
-            model_file=str(tokenizer_path)
-        )
-
-        inputs = (
-            InputRecorder(  # pyre-fixme[16]
-                tokenizer,
-                calibration_seq_length,
-                None,  # input_prep_func
-                pad_calibration_inputs,
-                model.vocab_size,
-            )
-            .record_inputs(
-                calibration_tasks,
-                calibration_limit,
-            )
-            .get_inputs()
-        )
-
-        gptq_quantizer = Int8DynActInt4WeightGPTQQuantizer(
-            blocksize,
-            percdamp,
-            group_size,
-        )  # TODO: separate computation and checkpoint dtype for GPTQ.
-        model = gptq_quantizer.quantize(model, inputs)
-        return model
     elif qmode == "vulkan_4w":
         from executorch.backends.vulkan._passes import VkInt4WeightOnlyQuantizer