[CI/Test] improve robustness of test (vllm_runner) (#5357)

[CI/Test] improve robustness of test by replacing del with context manager (vllm_runner) (#5357)

tests/quantization/test_bitsandbytes.py

@@ -16,65 +16,65 @@ capability = capability[0] * 10 + capability[1]
     capability < QUANTIZATION_METHODS['bitsandbytes'].get_min_capability(),
     reason='bitsandbytes is not supported on this GPU type.')
 def test_load_bnb_model(vllm_runner) -> None:
-    llm = vllm_runner('huggyllama/llama-7b',
-                      quantization='bitsandbytes',
-                      load_format='bitsandbytes',
-                      enforce_eager=True)
+    with vllm_runner('huggyllama/llama-7b',
+                     quantization='bitsandbytes',
+                     load_format='bitsandbytes',
+                     enforce_eager=True) as llm:
 
-    model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model
+        model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model  # noqa: E501
 
-    # check the weights in MLP & SelfAttention are quantized to torch.uint8
-    qweight = model.model.layers[0].mlp.gate_up_proj.qweight
-    assert qweight.dtype == torch.uint8, (
-        f'Expected gate_up_proj dtype torch.uint8 but got {qweight.dtype}')
+        # check the weights in MLP & SelfAttention are quantized to torch.uint8
+        qweight = model.model.layers[0].mlp.gate_up_proj.qweight
+        assert qweight.dtype == torch.uint8, (
+            f'Expected gate_up_proj dtype torch.uint8 but got {qweight.dtype}')
 
-    qweight = model.model.layers[0].mlp.down_proj.qweight
-    assert qweight.dtype == torch.uint8, (
-        f'Expected down_proj dtype torch.uint8 but got {qweight.dtype}')
+        qweight = model.model.layers[0].mlp.down_proj.qweight
+        assert qweight.dtype == torch.uint8, (
+            f'Expected down_proj dtype torch.uint8 but got {qweight.dtype}')
 
-    qweight = model.model.layers[0].self_attn.o_proj.qweight
-    assert qweight.dtype == torch.uint8, (
-        f'Expected o_proj dtype torch.uint8 but got {qweight.dtype}')
+        qweight = model.model.layers[0].self_attn.o_proj.qweight
+        assert qweight.dtype == torch.uint8, (
+            f'Expected o_proj dtype torch.uint8 but got {qweight.dtype}')
 
-    qweight = model.model.layers[0].self_attn.qkv_proj.qweight
-    assert qweight.dtype == torch.uint8, (
-        f'Expected qkv_proj dtype torch.uint8 but got {qweight.dtype}')
+        qweight = model.model.layers[0].self_attn.qkv_proj.qweight
+        assert qweight.dtype == torch.uint8, (
+            f'Expected qkv_proj dtype torch.uint8 but got {qweight.dtype}')
 
-    # some weights should not be quantized
-    weight = model.lm_head.weight
-    assert weight.dtype != torch.uint8, (
-        'lm_head weight dtype should not be torch.uint8')
+        # some weights should not be quantized
+        weight = model.lm_head.weight
+        assert weight.dtype != torch.uint8, (
+            'lm_head weight dtype should not be torch.uint8')
 
-    weight = model.model.embed_tokens.weight
-    assert weight.dtype != torch.uint8, (
-        'embed_tokens weight dtype should not be torch.uint8')
+        weight = model.model.embed_tokens.weight
+        assert weight.dtype != torch.uint8, (
+            'embed_tokens weight dtype should not be torch.uint8')
 
-    weight = model.model.layers[0].input_layernorm.weight
-    assert weight.dtype != torch.uint8, (
-        'input_layernorm weight dtype should not be torch.uint8')
+        weight = model.model.layers[0].input_layernorm.weight
+        assert weight.dtype != torch.uint8, (
+            'input_layernorm weight dtype should not be torch.uint8')
 
-    weight = model.model.layers[0].post_attention_layernorm.weight
-    assert weight.dtype != torch.uint8, (
-        'input_layernorm weight dtype should not be torch.uint8')
+        weight = model.model.layers[0].post_attention_layernorm.weight
+        assert weight.dtype != torch.uint8, (
+            'input_layernorm weight dtype should not be torch.uint8')
 
-    # check the output of the model is expected
-    sampling_params = SamplingParams(temperature=0.0,
-                                     logprobs=1,
-                                     prompt_logprobs=1,
-                                     max_tokens=8)
+        # check the output of the model is expected
+        sampling_params = SamplingParams(temperature=0.0,
+                                         logprobs=1,
+                                         prompt_logprobs=1,
+                                         max_tokens=8)
 
-    prompts = ['That which does not kill us', 'To be or not to be,']
-    expected_outputs = [
-        'That which does not kill us makes us stronger.',
-        'To be or not to be, that is the question.'
-    ]
-    outputs = llm.generate(prompts, sampling_params=sampling_params)
+        prompts = ['That which does not kill us', 'To be or not to be,']
+        expected_outputs = [
+            'That which does not kill us makes us stronger.',
+            'To be or not to be, that is the question.'
+        ]
+        outputs = llm.generate(prompts, sampling_params=sampling_params)
 
-    assert len(outputs) == len(prompts)
+        assert len(outputs) == len(prompts)
 
-    for index in range(len(outputs)):
-        # compare the first line of the output
-        actual_output = outputs[index][1][0].split('\n', 1)[0]
-        expected_output = expected_outputs[index].split('\n', 1)[0]
-        assert actual_output == expected_output, (
-            f'Expected: {expected_output}, but got: {actual_output}')
+        for index in range(len(outputs)):
+            # compare the first line of the output
+            actual_output = outputs[index][1][0].split('\n', 1)[0]
+            expected_output = expected_outputs[index].split('\n', 1)[0]
+            assert actual_output == expected_output, (
+                f'Expected: {expected_output}, but got: {actual_output}')