[Bugfix] Enable some fp8 and quantized fullgraph tests (#10171)

Signed-off-by: Bill Nell <bill@neuralmagic.com>

tests/compile/utils.py

@@ -9,29 +9,26 @@ from vllm.platforms import current_platform
 
 TEST_MODELS = [
     ("facebook/opt-125m", {}),
-    # TODO: add fake implementation for compressed-tensors
-    # ("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
-    #     "dtype": torch.float16,
-    #     "quantization": "compressed-tensors"
-    # }),
+    ("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
+        "dtype": torch.float16,
+        "quantization": "compressed-tensors"
+    }),
     ("neuralmagic/Meta-Llama-3-8B-Instruct-FP8", {
         "dtype": torch.float16,
         "quantization": "fp8"
     }),
-    # TODO: add fake implementation for compressed-tensors
-    # ("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", {
-    #     "quantization": "compressed-tensors"
-    # }),
+    ("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", {
+        "quantization": "compressed-tensors"
+    }),
     ("meta-llama/Meta-Llama-3-8B", {}),
 ]
 
-# TODO: enable in pytorch 2.5
-if False and is_quant_method_supported("aqlm"):  # noqa: SIM223
+if is_quant_method_supported("aqlm"):
     TEST_MODELS.append(("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf", {
         "quantization": "aqlm"
     }))
 
-# TODO: enable in pytorch 2.5
+# TODO: figure out why this fails.
 if False and is_quant_method_supported("gguf"):  # noqa: SIM223
     TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF", {
         "quantization": "gguf"
@@ -71,13 +68,13 @@ def check_full_graph_support(model,
     os.environ["VLLM_TORCH_COMPILE_LEVEL"] = str(optimization_level)
     os.environ["VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE"] = "1"
 
-    # Inductor doesn't support fp8 and the base meta llama uses too
-    # much memory.
-    quantization = model_kwargs.get("quantization")
-    if ((quantization == "fp8" or model == "meta-llama/Meta-Llama-3-8B")
+    # The base meta llama uses too much memory.
+    if (model == "meta-llama/Meta-Llama-3-8B"
             and optimization_level >= CompilationLevel.PIECEWISE):
         return
 
+    print(f"MODEL={model}")
+
     prompts = [
         "Hello, my name is",
         "The president of the United States is",