reworked on final review comments

Author: amitsrivastava78

keras/api/_tf_keras/keras/quantizers/__init__.py

@@ -7,6 +7,7 @@
 from keras.src.quantizers import deserialize as deserialize
 from keras.src.quantizers import get as get
 from keras.src.quantizers import serialize as serialize
+from keras.src.quantizers.gptq_config import GPTQConfig as GPTQConfig
 from keras.src.quantizers.quantizers import AbsMaxQuantizer as AbsMaxQuantizer
 from keras.src.quantizers.quantizers import Quantizer as Quantizer
 from keras.src.quantizers.quantizers import abs_max_quantize as abs_max_quantize

keras/api/quantizers/__init__.py

@@ -7,6 +7,7 @@
 from keras.src.quantizers import deserialize as deserialize
 from keras.src.quantizers import get as get
 from keras.src.quantizers import serialize as serialize
+from keras.src.quantizers.gptq_config import GPTQConfig as GPTQConfig
 from keras.src.quantizers.quantizers import AbsMaxQuantizer as AbsMaxQuantizer
 from keras.src.quantizers.quantizers import Quantizer as Quantizer
 from keras.src.quantizers.quantizers import abs_max_quantize as abs_max_quantize

keras/src/models/model.py

@@ -436,10 +436,9 @@ def quantize(self, mode, config=None, **kwargs):
 
         if mode == "gptq":
             if not isinstance(config, GPTQConfig):
-                raise TypeError(
-                    "When using 'gptq' mode, you must pass a `config` "
-                    "argument of type "
-                    "`keras.quantizers.gptq_config.GPTQConfig`."
+                raise ValueError(
+                    "The `config` argument must be of type "
+                    "`keras.quantizers.GPTQConfig`."
                 )
             # The config object's own quantize method drives the process
             config.quantize(self)

keras/src/models/model_test.py

@@ -1243,12 +1243,12 @@ def test_export_error(self):
 
 
 # Helper function to generate dummy data for quick testing.
-def dummy_dataset_generator(nsamples, seqlen, vocab_size=1000):
+def dummy_dataset_generator(num_samples, sequence_length, vocab_size=1000):
     """A generator that yields random numpy arrays for fast,
     self-contained tests."""
     rng = np.random.default_rng(seed=42)
-    for _ in range(nsamples):
-        yield rng.integers(low=0, high=vocab_size, size=(1, seqlen))
+    for _ in range(num_samples):
+        yield rng.integers(low=0, high=vocab_size, size=(1, sequence_length))
 
 
 # Helper function to build a simple transformer model.
@@ -1295,13 +1295,13 @@ def call(self, inputs):
 DATASETS = {
     "string_dataset": [long_text],
     "generator_dataset": lambda: dummy_dataset_generator(
-        nsamples=16, seqlen=128
+        num_samples=16, sequence_length=128
     ),
 }
 CONFIGS = {
     "default": {},
     "per_channel": {"group_size": -1},
-    "act_order": {"act_order": True},
+    "act_order": {"activation_order": True},
     "symmetric": {"symmetric": True},
 }
 
@@ -1315,9 +1315,9 @@ def _get_simple_model():
     # --- Error Scenarios ---
     (
         "gptq",
-        {"wbits": 4},  # Invalid config (dict, not GPTQConfig)
-        TypeError,
-        "must pass a `config` argument of type",
+        {"weight_bits": 4},  # Invalid config (dict, not GPTQConfig)
+        ValueError,
+        "The `config` argument must be of type",
         "gptq_with_invalid_config",
     ),
     (
@@ -1360,12 +1360,12 @@ def _run_gptq_test_on_dataset(self, dataset, **config_kwargs):
         base_config = {
             "dataset": dataset,
             "tokenizer": mock_tokenizer,
-            "wbits": W_BITS,
-            "nsamples": NUM_SAMPLES,
-            "seqlen": SEQUENCE_LENGTH,
+            "weight_bits": W_BITS,
+            "num_samples": NUM_SAMPLES,
+            "sequence_length": SEQUENCE_LENGTH,
             "group_size": 32,
             "symmetric": False,
-            "act_order": False,
+            "activation_order": False,
         }
 
         target_layer = model.layers[2].ffn.layers[0]