fix 3 bit packing regression， fixed #1278 (#1280)

* fix torch's pack()

* allow setting bits & quant backend

* add 3 bit test

* fix 3 bit packing in base

* revert data clone changes

* remove 3bits test in q4 cuda

* fix error was printed but ignored

* add delta windows size

* update scores

* fix score

Author: CSY-ModelCloud

gptqmodel/nn_modules/qlinear/__init__.py

@@ -344,23 +344,24 @@ def pack(self, linear, scales, zeros, g_idx=None):
         elif self.bits == 3:
             i = 0
             col = 0
-            for j in range(i, i + 10):
-                qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
-            i += 10
-            qzeros[:, col] |= zeros[:, i] << 30
-            col += 1
-            qzeros[:, col] |= (zeros[:, i] >> 2) & 1
-            i += 1
-            for j in range(i, i + 10):
-                qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
-            i += 10
-            qzeros[:, col] |= zeros[:, i] << 31
-            col += 1
-            qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
-            i += 1
-            for j in range(i, i + 10):
-                qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
-            i += 10
-            col += 1
+            while col < qzeros.shape[1]:
+                for j in range(i, i + 10):
+                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
+                i += 10
+                qzeros[:, col] |= zeros[:, i] << 30
+                col += 1
+                qzeros[:, col] |= (zeros[:, i] >> 2) & 1
+                i += 1
+                for j in range(i, i + 10):
+                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
+                i += 10
+                qzeros[:, col] |= zeros[:, i] << 31
+                col += 1
+                qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
+                i += 1
+                for j in range(i, i + 10):
+                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
+                i += 10
+                col += 1
 
         self.qzeros = t.from_numpy(qzeros.astype(self.pack_np_dtype))

tests/models/model_test.py

@@ -58,6 +58,7 @@ class ModelTest(unittest.TestCase):
     TORCH_DTYPE = "auto"
     BATCH_SIZE = "auto"
     LOAD_BACKEND = BACKEND.AUTO
+    QUANT_BACKEND = BACKEND.AUTO
     USE_VLLM = False
     INPUTS_MAX_LENGTH = 2048
     MODEL_MAX_LEN = 4096
@@ -83,6 +84,8 @@ class ModelTest(unittest.TestCase):
     LM_HEAD_LOSS_MAX_DELTA_PERCENT = 0.1  # ±10%
     EXPECT_LM_HEAD_LOSS = None
 
+    QUANTIZE_CONFIG_BITS = 4
+
     def assertInference(self, model, tokenizer=None, keywords=None, prompt=INFERENCE_PROMPT):
         # gptqmodel can auto init tokenizer internally
         if keywords is None:
@@ -148,7 +151,7 @@ def check_kernel(self, model, expected_kernels):
 
     def quantModel(self, model_id_or_path, trust_remote_code=False, torch_dtype="auto", need_eval=True, batch_size: int = 4, **kwargs):
         quantize_config = QuantizeConfig(
-            bits=4,
+            bits=self.QUANTIZE_CONFIG_BITS,
             group_size=128,
             format=self.QUANT_FORMAT,
             desc_act=self.DESC_ACT,
@@ -189,7 +192,7 @@ def quantModel(self, model_id_or_path, trust_remote_code=False, torch_dtype="aut
         is_ovis_model = model.__class__.__name__ == "OvisGPTQ"
         need_create_processor = is_image_to_text_model and not is_ovis_model
         if not is_quantized:
-            model.quantize(calibration_dataset, batch_size=batch_size)
+            model.quantize(calibration_dataset, backend=self.QUANT_BACKEND, batch_size=batch_size)
 
             self.check_kernel(model, self.KERNEL_QUANT)
 

tests/test_bits.py

@@ -54,14 +54,14 @@ class TestBits(unittest.TestCase):
         BACKEND.MARLIN: MarlinQuantLinear,
     }
 
-    QUANT_ARC_MAX_DELTA_FLOOR_PERCENT = 0.025  # -2.5%
-    QUANT_ARC_MAX_POSITIVE_DELTA_CEIL_PERCENT = 0.025  # +2.5%
+    QUANT_ARC_MAX_DELTA_FLOOR_PERCENT = 0.1
+    QUANT_ARC_MAX_POSITIVE_DELTA_CEIL_PERCENT = 0.1
 
     CUDA_QLINEAR_QUANTIZED_MODEL_ARC_CHALLENGE_EXPECTS = {
-        2: {'acc,none': 0.22610921501706485, 'acc_norm,none': 0.2909556313993174},
-        3: {'acc,none': 0.21245733788395904, 'acc_norm,none': 0.24744027303754265},
-        4: {'acc,none': 0.2738907849829352, 'acc_norm,none': 0.3122866894197952},
-        8: {'acc,none': 0.2841296928327645, 'acc_norm,none': 0.302901023890785},
+        2: {'acc,none': 0.2175767918088737, 'acc_norm,none': 0.26535836177474403},
+        3: {'acc,none': 0.22696245733788395, 'acc_norm,none': 0.2627986348122867},
+        4: {'acc,none': 0.26621160409556316, 'acc_norm,none': 0.3148464163822526},
+        8: {'acc,none': 0.29948805460750855, 'acc_norm,none': 0.3293515358361775},
     }
 
     def calculatorPer(self, filter, value, base_value):
@@ -92,22 +92,29 @@ def test_bits(self):
         # quantize
         model_id = "/monster/data/model/TinyLlama-1.1B-Chat-v1.0"
         tokenizer = AutoTokenizer.from_pretrained(model_id)
-        dataset = [
-            "gptqmodel is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."]
+        dataset = ["gptqmodel is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."]
         calibration_dataset = [tokenizer(example) for example in dataset]
+
+        errors = []
         for quant_backend in self.pack_backends:
             supports_bits = self.QLINEAR_DICT[quant_backend].SUPPORTS_BITS
             for bits in supports_bits:
-                print("-----------------------quant-----------------------")
+                print(f"-----------------------quant backend: {quant_backend}-- bits: {bits} ---------------------")
                 quantize_config = QuantizeConfig(bits=bits, group_size=128, sym=True, desc_act=False)
                 print(f"bits: {quantize_config.bits}, quant_backend: {quant_backend} start quant")
                 try:
                     self.quant_and_eval(calibration_dataset, model_id, quant_backend, quantize_config, tokenizer)
                 except Exception:
-                    print(f"bits:  {quantize_config.bits}, quant_backend: {quant_backend} An error occurred")
+                    error_log=f"bits:  {quantize_config.bits}, quant_backend: {quant_backend} An error occurred"
+                    print(error_log)
+                    errors.append(error_log)
+
                     traceback.print_exc()
+
                     continue
 
+        self.assertTrue(len(errors) == 0, '\n'.join(errors))
+
     def quant_and_eval(self, calibration_dataset, model_id, quant_backend, quantize_config, tokenizer):
         model = GPTQModel.load(
             model_id,
@@ -127,11 +134,7 @@ def quant_and_eval(self, calibration_dataset, model_id, quant_backend, quantize_
                     # Skip inference_backend that does not support the current bits
                     continue
 
-                try:
-                    self.eval(inference_backend, quant_backend, quantize_config, tmp_dir)
-                except Exception:
-                    traceback.print_exc()
-                    continue
+                self.eval(inference_backend, quant_backend, quantize_config, tmp_dir)
 
     def eval(self, inference_backend, quant_backend, quantize_config, tmp_dir):
         print("-----------------------eval-----------------------")
@@ -165,8 +168,7 @@ def eval(self, inference_backend, quant_backend, quantize_config, tmp_dir):
             metric: value for metric, value in results['results'].get(TASK_NAME, {}).items()
             if metric != 'alias' and 'stderr' not in metric
         }
-        print(
-            f"bits is: {quantize_config.bits}, quant_backend: {quant_backend}, inference_backend: {inference_backend} -> task_results: {task_results}")
+        print(f"bits is: {quantize_config.bits}, quant_backend: {quant_backend}, inference_backend: {inference_backend} -> task_results: {task_results}")
         del model
 
         self.check_results(quantize_config.bits, task_results)

tests/test_q4_cuda.py

@@ -16,13 +16,16 @@
 
 # -- do not touch
 import os
+import tempfile
+
+from gptqmodel.utils import Perplexity
 
 os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
 # -- end do not touch
 
 
 import torch  # noqa: E402
-from gptqmodel import BACKEND, GPTQModel  # noqa: E402
+from gptqmodel import BACKEND, GPTQModel, QuantizeConfig  # noqa: E402
 from models.model_test import ModelTest  # noqa: E402
 from parameterized import parameterized  # noqa: E402
 from transformers import AutoTokenizer  # noqa: E402
@@ -74,4 +77,3 @@ def test_generation_desc_act_false(self, torch_dtype, device):
         self.assertInference(model=model_q,tokenizer=self.tokenizer)
         # This one does not.
         self.assertInference(model=model_q.model,tokenizer=self.tokenizer)
-