Actually make the run_compressed test useful

tests/llmcompressor/transformers/compression/run_compressed_configs/fp8_dynamic.yaml

@@ -1,3 +1,4 @@
 cadence: "commit"
 test_type: "regression"
-model_stub: "nm-testing/tinyllama-fp8-dynamic-compressed"
+model_stub: "nm-testing/tinyllama-fp8-dynamic-compressed"
+empty_model: "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T" 

tests/llmcompressor/transformers/compression/run_compressed_configs/w4a16.yaml

@@ -1,3 +1,4 @@
 cadence: "commit"
 test_type: "regression"
-model_stub: "nm-testing/tinyllama-w4a16-compressed"
+model_stub: "nm-testing/tinyllama-w4a16-compressed"
+empty_model: "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"

tests/llmcompressor/transformers/compression/run_compressed_configs/w8a16_dense.yaml

@@ -1,3 +1,4 @@
 cadence: "commit"
 test_type: "regression"
-model_stub: "nm-testing/tinyllama-w8a16-dense"
+model_stub: "nm-testing/tinyllama-w8a16-dense"
+empty_model: "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"

tests/llmcompressor/transformers/compression/run_compressed_configs/w8a8.yaml

@@ -1,3 +1,4 @@
 cadence: "commit"
 test_type: "regression"
-model_stub: "nm-testing/tinyllama-w8a8-compressed"
+model_stub: "nm-testing/tinyllama-w8a8-compressed"
+empty_model: "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"

tests/llmcompressor/transformers/compression/test_run_compressed.py

@@ -3,8 +3,11 @@
 import unittest
 
 import torch
+from compressed_tensors import QUANTIZATION_CONFIG_NAME
+from compressed_tensors.compressors import ModelCompressor
+from compressed_tensors.quantization import QuantizationStatus
 from parameterized import parameterized_class
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 
 from tests.testing_utils import parse_params, requires_gpu, requires_torch
 
@@ -16,6 +19,7 @@
 @parameterized_class(parse_params(CONFIG_DIR))
 class TestQuantizationMatches(unittest.TestCase):
     model_stub = None
+    empty_model = None
 
     @classmethod
     def setUpClass(cls):
@@ -29,25 +33,34 @@ def setUpClass(cls):
             device_map="auto",
             # run_compressed=True, # TODO: Give option on HFQuantizer
         )
+        # TODO: Use ModelCompressor until decompression is supported through
+        # HFQuant/run_compressed can be turned off.
         cls.uncompressed_model = AutoModelForCausalLM.from_pretrained(
-            cls.model_stub,
-            torch_dtype="auto",
-            device_map="auto",
-            # run_compressed=False, # TODO: Give option on HFQuantizer
+            cls.empty_model,
+            torch_dtype=cls.compressed_model.dtype,
+            device_map=cls.compressed_model.device,
+        )
+        config = AutoConfig.from_pretrained(cls.model_stub)
+        compression_config = getattr(config, QUANTIZATION_CONFIG_NAME, None)
+        cls.compressor = ModelCompressor.from_compression_config(compression_config)
+        cls.compressor.quantization_config.quantization_status = (
+            QuantizationStatus.FROZEN
+        )
+        cls.compressor.decompress(
+            model_path=cls.model_stub, model=cls.uncompressed_model
         )
 
         cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_stub)
-        cls.device = cls.compressed_model.device
 
     def test_compressed_matches_uncompressed(self):
         SAMPLE_INPUT = [
             "I love 4-bit quantization because",
-            "What is the capital of Paris?",
+            "What is the capital of France?",
             "def fibonacci(n):",
         ]
 
         inputs = self.tokenizer(SAMPLE_INPUT, return_tensors="pt", padding=True).to(
-            self.device
+            self.compressed_model.device
         )
         compressed_output = self.tokenizer.batch_decode(
             self.compressed_model.generate(**inputs, max_length=50)