[Misc] Replace os environ to monkeypatch in test suite (#14516)

Signed-off-by: sibi <85477603+t-sibiraj@users.noreply.github.com>
Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: Aaron Pham <contact@aarnphm.xyz>

tests/compile/test_full_graph.py

@@ -1,22 +1,115 @@
 # SPDX-License-Identifier: Apache-2.0
 
-import pytest
+from __future__ import annotations
 
+from typing import Any
+
+import pytest
+import torch
+
+from tests.quantization.utils import is_quant_method_supported
+from vllm import LLM, SamplingParams
 from vllm.config import CompilationLevel
+from vllm.platforms import current_platform
 
 from ..utils import fork_new_process_for_each_test
-from .utils import TEST_MODELS, check_full_graph_support
 
 
-@pytest.mark.parametrize("model_info", TEST_MODELS)
+@pytest.fixture(params=None, name="model_info")
+def models_list_fixture(request):
+    TEST_MODELS: list[tuple[str, dict[str, Any]]] = [
+        ("facebook/opt-125m", {}),
+        ("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
+            "dtype": torch.float16,
+            "quantization": "compressed-tensors"
+        }),
+        ("neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic", {
+            "dtype": torch.float16,
+            "quantization": "compressed-tensors"
+        }),
+        ("neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8", {
+            "quantization": "compressed-tensors"
+        }),
+        ("meta-llama/Llama-3.2-1B-Instruct", {}),
+    ]
+
+    if is_quant_method_supported("aqlm"):
+        TEST_MODELS.append(("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf", {
+            "quantization": "aqlm"
+        }))
+
+    # 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"
+        }))
+
+    if is_quant_method_supported("gptq"):
+        TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", {
+            "quantization": "gptq"
+        }))
+
+    if is_quant_method_supported("gptq_marlin"):
+        TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", {
+            "quantization": "gptq_marlin"
+        }))
+
+    if is_quant_method_supported("gptq_marlin_24"):
+        TEST_MODELS.append(("alexm-nm/tinyllama-24-marlin24-4bit-g128", {
+            "quantization": "gptq_marlin_24"
+        }))
+
+    if is_quant_method_supported("marlin"):
+        TEST_MODELS.append(
+            ("robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin", {
+                "quantization": "marlin"
+            }))
+
+    if not current_platform.is_rocm() and is_quant_method_supported("awq"):
+        TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {
+            "quantization": "AWQ"
+        }))
+
+    return TEST_MODELS
+
+
 @pytest.mark.parametrize(
     "optimization_level",
-    [CompilationLevel.DYNAMO_ONCE, CompilationLevel.PIECEWISE])
+    [CompilationLevel.DYNAMO_ONCE, CompilationLevel.PIECEWISE],
+)
+@pytest.mark.parametrize("model_info", "", indirect=True)
 @fork_new_process_for_each_test
-def test_full_graph(model_info, optimization_level):
-    model = model_info[0]
-    model_kwargs = model_info[1]
-    check_full_graph_support(model,
-                             model_kwargs,
-                             optimization_level,
-                             tp_size=1)
+def test_full_graph(
+    monkeypatch: pytest.MonkeyPatch,
+    model_info: tuple[str, dict[str, Any]],
+    optimization_level: int,
+):
+    model, model_kwargs = model_info
+
+    with monkeypatch.context() as m:
+        # make sure these models can be captured in full graph mode
+        m.setenv("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1")
+        print(f"MODEL={model}")
+
+        prompts = [
+            "Hello, my name is",
+            "The president of the United States is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        sampling_params = SamplingParams(temperature=0)
+        llm = LLM(
+            model=model,
+            enforce_eager=True,
+            tensor_parallel_size=1,
+            disable_custom_all_reduce=True,
+            compilation_config=optimization_level,
+            **model_kwargs,
+        )
+        outputs = llm.generate(prompts, sampling_params)
+
+        # Print the outputs.
+        for output in outputs:
+            prompt = output.prompt
+            generated_text = output.outputs[0].text
+            print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")