[Tests] Fix GPTQ Tests (vllm-project#1692)

SUMMARY:
- The current tests are failing because when loading the tinystories
model, the lm_head is ending up with device type "meta"
- This model is generally problematic so we swap to use TinyLlama 
- With the size of the model being large, we target just one layer for
quantization to contain runtime, while updating the asserts to be
reflective of the just one layer being quantized

TESTING:
- All tests pass with these changes

Author: dsikka

tests/llmcompressor/transformers/gptq/test_oneshot.py

@@ -20,14 +20,15 @@
                         type: "int"
                         symmetric: true
                         strategy: "channel"
-                    targets: ["Linear"]
+                    targets: ["re:.*model.layers.2.self_attn.q_proj$"]
 """
 
 recipe_modifier_full = GPTQModifier(
     ignore=["lm_head"],
     config_groups={
         "group_0": QuantizationScheme(
-            targets=["Linear"], weights=QuantizationArgs(num_bits=4, strategy="channel")
+            targets=["re:.*model.layers.2.self_attn.q_proj$"],
+            weights=QuantizationArgs(num_bits=4, strategy="channel"),
         )
     },
 )
@@ -36,18 +37,18 @@
     ignore=["lm_head"],
     config_groups={
         "group_0": QuantizationScheme(
-            targets=["Linear"],
+            targets=["re:.*model.layers.2.self_attn.q_proj$"],
             weights=QuantizationArgs(num_bits=4, strategy="group", group_size=128),
         )
     },
 )
 
 recipe_modifier_shorthand_a = GPTQModifier(
-    ignore=["lm_head"], targets="Linear", scheme="W4A16"
+    ignore=["lm_head"], targets="re:.*model.layers.2.self_attn.q_proj$", scheme="W4A16"
 )
 
 recipe_modifier_shorthand_b = GPTQModifier(
-    ignore=["lm_head"], scheme={"W4A16": ["Linear"]}
+    ignore=["lm_head"], scheme={"W4A16": ["re:.*model.layers.2.self_attn.q_proj$"]}
 )
 
 
@@ -65,7 +66,7 @@ def setUp(self):
         import torch
 
         self.output = "./oneshot_output"
-        self.model = "Xenova/llama2.c-stories110M"
+        self.model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
         self.dataset = "open_platypus"
         self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
@@ -95,17 +96,17 @@ def test_oneshot_application(self):
         assert quantization_config is not None
 
         # check config is set properly
-        assert quantization_config.ignore == ["lm_head"]
+        assert "lm_head" in quantization_config.ignore
         assert len(quantization_config.config_groups) == 1
         quant_scheme = quantization_config.config_groups["group_0"]
         assert isinstance(quant_scheme, QuantizationScheme)
-        assert quant_scheme.targets == ["Linear"]
+        assert quant_scheme.targets == ["re:.*model.layers.2.self_attn.q_proj$"]
         weight_args = quantization_config.config_groups["group_0"].weights
         assert isinstance(weight_args, QuantizationArgs)
         assert weight_args.num_bits == 4
 
         # Check a specific layer is quantized
-        targetted_linear_layer = model_loaded.model.layers[0].self_attn.k_proj
+        targetted_linear_layer = model_loaded.model.layers[2].self_attn.q_proj
         assert hasattr(targetted_linear_layer, "quantization_scheme")
 
         # Check lm-head is not quantized