merge main branch to alg_ext (#970)

Author: wenhuach21

auto_round/alg_ext.abi3.so

@@ -2741,6 +2741,7 @@ def _quantize_blocks(
                 else:
                     logger.info("using algorithm extension for quantization.")
             except (ImportError, ModuleNotFoundError):
+                logger.error("algorithm extension import error, fallback to default mode")
                 quantize_block = self._quantize_block
         else:
             quantize_block = self._quantize_block

auto_round/compressors/base.py

@@ -716,6 +716,9 @@ def test_alg_ext(self):
         ar = AutoRound(model_name, scheme="W2A16", iters=1, nsamples=1, enable_alg_ext=True)
         ar.quantize()
 
+    def test_alg_ext_import(self):
+        from auto_round.alg_ext import quantize_block_ext
+
     def test_invalid_layer_config(self):
         with self.assertRaises(ValueError):
             layer_config = {"model.decoder.layers.2.self_attnx": {"bits": 2}}

test/test_cpu/test_autoround.py

@@ -0,0 +1,40 @@
+import shutil
+import sys
+import unittest
+
+sys.path.insert(0, "../..")
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from auto_round import AutoRound, AutoRoundConfig
+from auto_round.eval.evaluation import simple_evaluate_user_model
+
+
+class TestAlgExt(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(self):
+        self.model_name = "/models/opt-125m"
+        self.save_folder = "./saved"
+
+    @classmethod
+    def tearDownClass(self):
+        shutil.rmtree(self.save_folder, ignore_errors=True)
+        shutil.rmtree("runs", ignore_errors=True)
+
+    def test_2bits(self):
+        model_name = "/models/opt-125m"
+        ar = AutoRound(model=model_name, bits=2, group_size=64, enable_alg_ext=True)
+        ar.quantize_and_save(self.save_folder)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.save_folder,
+            device_map="auto",
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(self.save_folder)
+        result = simple_evaluate_user_model(model, tokenizer, batch_size=64, tasks="lambada_openai")
+        print(result["results"]["lambada_openai"]["acc,none"])
+        # wo alg ext 0.2084, with 0.2364
+        self.assertGreater(result["results"]["lambada_openai"]["acc,none"], 0.22)
+        shutil.rmtree(self.save_folder, ignore_errors=True)