[TRTLLM-6975][test] Add multi-turn test cases for VLM models

examples/llm-api/quickstart_multimodal.py

@@ -108,6 +108,15 @@ def add_multimodal_args(parser):
                         type=str,
                         default="cpu",
                         help="The device to have the input on.")
+    # Add multiturn conversation related parameters
+    parser.add_argument("--multiturn",
+                        action="store_true",
+                        help="Enable multi-turn conversation mode.")
+    parser.add_argument(
+        "--conversation_turns",
+        type=int,
+        default=2,
+        help="Number of conversation turns for automated testing.")
     return parser
 
 
@@ -162,6 +171,80 @@ def main():
             open(os.path.join(llm._hf_model_dir, 'config.json')))['model_type']
     assert model_type in ALL_SUPPORTED_MULTIMODAL_MODELS, f"Unsupported model_type: {model_type}"
 
+    # If multiturn mode is enabled
+    if args.multiturn:
+        # Run predefined multiturn conversation examples
+        assert args.prompt is not None, "Please provide a prompt for multiturn conversation."
+        assert args.media is not None, "Please provide media for multiturn conversation."
+        # Determine how many turns to run
+        max_turns = min(args.conversation_turns, len(args.prompt))
+        generated_outputs = []  # Store generated outputs for return
+
+        # Initialize conversation history with the first prompt
+        conversation_history = args.prompt[0] if args.prompt else ""
+
+        for i in range(max_turns):
+            print(f"\n--- Turn {i+1} ---")
+
+            try:
+                # Use multimodal input loader to process input with conversation context
+                # Use accumulated conversation history instead of just the current prompt
+                cur_prompt = conversation_history
+                inputs = default_multimodal_input_loader(
+                    tokenizer=llm.tokenizer,
+                    model_dir=llm._hf_model_dir,
+                    model_type=model_type,
+                    modality=args.modality,
+                    prompts=[cur_prompt],
+                    media=args.media,
+                    image_data_format="pt",
+                    num_frames=8,
+                    device="cpu")
+
+                lora_request = None
+                if args.load_lora:
+                    if model_class is None:
+                        raise ValueError(
+                            "model_class must be provided when load_lora is True"
+                        )
+                    lora_request = model_class.lora_request(
+                        len(inputs), args.modality, llm._hf_model_dir)
+
+                # Generate response
+                outputs = llm.generate(inputs,
+                                       sampling_params,
+                                       lora_request=lora_request)
+                assert outputs and len(
+                    outputs) > 0 and outputs[0].outputs and len(
+                        outputs[0].outputs) > 0
+                response = outputs[0].outputs[0].text.strip()
+
+                # Store generated output
+                generated_outputs.append({
+                    "turn": i + 1,
+                    "user_input": cur_prompt,
+                    "assistant_response": response,
+                    "media": args.media
+                })
+
+                conversation_history = conversation_history + "\n" + response
+                if i + 1 < len(args.prompt):
+                    conversation_history = conversation_history + "\n" + args.prompt[
+                        i + 1]
+
+            except Exception as e:
+                print(f"Error in turn {i+1}: {e}")
+                import traceback
+                traceback.print_exc()
+                continue
+
+        for i, output in enumerate(generated_outputs):
+            print(
+                f"[{i}] Prompt: {output['user_input']!r}, Generated text: {output['assistant_response']!r}"
+            )
+        return
+
+    # Original single-turn processing logic
     # set prompts and media to example prompts and images if they are not provided
     if args.prompt is None:
         args.prompt = example_medias_and_prompts[args.modality]["prompt"]

tests/integration/defs/accuracy/references/gsm8k.yaml

@@ -19,6 +19,13 @@ meta-llama/Llama-3.3-70B-Instruct:
     accuracy: 84.08
 meta-llama/Llama-4-Maverick-17B-128E-Instruct:
   - accuracy: 92.20
+  - quant_algo: FP8
+    kv_cache_quant_algo: FP8
+    accuracy: 92.20
+  - quant_algo: FP8
+    kv_cache_quant_algo: FP8
+    spec_dec_algo: Eagle
+    accuracy: 92.20
 meta-llama/Llama-4-Scout-17B-16E-Instruct:
   - accuracy: 89.70
   - quant_algo: NVFP4

tests/integration/defs/accuracy/references/mmlu.yaml

@@ -73,6 +73,9 @@ meta-llama/Llama-4-Maverick-17B-128E-Instruct:
     kv_cache_quant_algo: FP8
     spec_dec_algo: Eagle
     accuracy: 86.40
+  - quant_algo: FP8
+    kv_cache_quant_algo: FP8
+    accuracy: 86.40
 meta-llama/Llama-4-Scout-17B-16E-Instruct:
   - accuracy: 80.00
   - quant_algo: NVFP4

tests/integration/defs/accuracy/test_disaggregated_serving.py

@@ -246,7 +246,7 @@ def run_parallel_test(model_name: str, model_path: str, ctx_pp: int,
     total_ctx_gpus = ctx_tp * ctx_pp * ctx_instances
     total_gen_gpus = gen_tp * gen_pp * gen_instances
     if total_ctx_gpus + total_gen_gpus > get_device_count():
-        pytest.fail(
+        pytest.skip(
             f"Not enough devices for {ctx_instances} ctx instances (ctx_pp={ctx_pp}*ctx_tp={ctx_tp}) + {gen_instances} gen instances (gen_pp={gen_pp}*gen_tp={gen_tp}), total: {total_ctx_gpus + total_gen_gpus}"
         )
 
@@ -378,6 +378,7 @@ def test_ngram(self):
             task = GSM8K(self.MODEL_NAME)
             task.evaluate(llm)
 
+    @skip_pre_hopper
     @parametrize_with_ids("overlap_scheduler", [True, False])
     @parametrize_with_ids("eagle3_one_model", [True, False])
     def test_eagle3(self, overlap_scheduler, eagle3_one_model):
@@ -461,6 +462,7 @@ def test_multi_instance(self, testset):
 
 @pytest.mark.skip_less_device_memory(140000)
 @pytest.mark.timeout(3600)
+@pytest.mark.skip_less_device(4)
 class TestLlama4ScoutInstruct(LlmapiAccuracyTestHarness):
     MODEL_NAME = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
     MODEL_PATH = f"{llm_models_root()}/llama4-models/Llama-4-Scout-17B-16E-Instruct"
@@ -540,6 +542,7 @@ def test_nixl_backend(self):
     @parametrize_with_ids("overlap_scheduler", [True, False])
     @parametrize_with_ids("mtp_nextn",
                           [0, pytest.param(2, marks=skip_pre_hopper)])
+    @pytest.mark.skip_less_device(4)
     def test_auto_dtype(self, overlap_scheduler, mtp_nextn):
         ctx_server_config = {"disable_overlap_scheduler": True}
         gen_server_config = {"disable_overlap_scheduler": not overlap_scheduler}
@@ -671,6 +674,7 @@ def test_nixl_backend(self):
             task.evaluate(llm)
 
     @pytest.mark.parametrize("overlap_scheduler", [False, True])
+    @skip_pre_hopper
     def test_auto_dtype(self, overlap_scheduler):
         ctx_server_config = {
             "disable_overlap_scheduler": True,

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -695,6 +695,7 @@ class TestMistralSmall24B(LlmapiAccuracyTestHarness):
     MODEL_NAME = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
     MODEL_PATH = f"{llm_models_root()}/Mistral-Small-3.1-24B-Instruct-2503"
 
+    @pytest.mark.skip_less_device_memory(80000)
     def test_auto_dtype(self):
         with LLM(self.MODEL_PATH) as llm:
             task = CnnDailymail(self.MODEL_NAME)
@@ -1033,7 +1034,7 @@ def test_cute_dsl_fp8_block_scales(
             max_num_streams=3) if torch_compile else None)
         pytorch_config = dict(
             disable_overlap_scheduler=not overlap_scheduler,
-            use_cuda_graph=cuda_graph,
+            cuda_graph_config=CudaGraphConfig() if cuda_graph else None,
             torch_compile_config=torch_compile_config,
             moe_config=MoeConfig(backend="CUTEDSL"),
         )
@@ -1191,7 +1192,7 @@ def test_cute_dsl_fp8_block_scales_4gpus(
             max_num_streams=3) if torch_compile else None)
         pytorch_config = dict(
             disable_overlap_scheduler=not overlap_scheduler,
-            use_cuda_graph=cuda_graph,
+            cuda_graph_config=CudaGraphConfig() if cuda_graph else None,
             torch_compile_config=torch_compile_config,
             moe_config=MoeConfig(backend="CUTEDSL"),
         )

tests/integration/defs/test_e2e.py

@@ -2051,7 +2051,7 @@ def test_ptp_quickstart_advanced_8gpus(llm_root, llm_venv, model_name,
 def test_ptp_quickstart_advanced_8gpus_chunked_prefill_sq_22k(
         llm_root, llm_venv, model_name, model_path, cuda_graph):
     print(f"Testing {model_name} on 8 GPUs.")
-    example_root = Path(os.path.join(llm_root, "examples", "pytorch"))
+    example_root = Path(os.path.join(llm_root, "examples", "llm-api"))
     cmd = [
         str(example_root / "quickstart_advanced.py"),
         "--enable_chunked_prefill",
@@ -2076,10 +2076,12 @@ def test_ptp_quickstart_advanced_8gpus_chunked_prefill_sq_22k(
 @pytest.mark.skip_less_device_memory(80000)
 @pytest.mark.skip_less_device(2)
 @pytest.mark.parametrize("model_name,model_path", [
-    ("Llama3.1-70B-BF16", "llama-3.1-model/Meta-Llama-3.1-70B"),
     ('Nemotron-Super-49B-v1-BF16',
      'nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1'),
     ("Mixtral-8x7B-BF16", "Mixtral-8x7B-Instruct-v0.1"),
+    pytest.param('Llama3.1-70B-BF16',
+                 'llama-3.1-model/Meta-Llama-3.1-70B',
+                 marks=pytest.mark.skip_less_device_memory(95000)),
 ])
 def test_ptp_quickstart_advanced_2gpus_sm120(llm_root, llm_venv, model_name,
                                              model_path):
@@ -2521,6 +2523,106 @@ def test_ptp_quickstart_multimodal_2gpu(llm_root, llm_venv, model_name,
     print("All answers are correct!")
 
 
+@pytest.mark.skip_less_device_memory(80000)
+@pytest.mark.parametrize("model_name,model_path", [
+    ("gemma-3-27b-it", "gemma/gemma-3-27b-it"),
+    ("mistral-small-3.1-24b-instruct", "Mistral-Small-3.1-24B-Instruct-2503"),
+    ("Phi-4-multimodal-instruct", "multimodals/Phi-4-multimodal-instruct"),
+])
+def test_ptp_quickstart_multimodal_multiturn(llm_root, llm_venv, model_name,
+                                             model_path):
+    example_root = Path(os.path.join(llm_root, "examples", "llm-api"))
+    test_data_root = Path(
+        os.path.join(llm_models_root(), "multimodals", "test_data"))
+
+    print(f"Accuracy test {model_name} image mode with example inputs.")
+
+    # Define accuracy inputs for image modality
+    accuracy_inputs = {
+        "image": {
+            "prompt": [
+                "Describe what you see in this image.",
+                "How would you describe the atmosphere of this scene?",
+            ],
+            "media": [
+                str(test_data_root / "inpaint.png"),
+            ],
+        }
+    }
+
+    # Define expected keywords for each model
+    expected_keywords = {
+        "gemma-3-27b-it": {
+            "image": [
+                ["half", "dome", "yosemite", "landmark", "rounded"],
+                ["atmosphere", "peaceful", "majestic", "calm", "quiet"],
+            ],
+        },
+        "mistral-small-3.1-24b-instruct": {
+            "image": [
+                ["depicts", "landscape", "rock", "sky", "high", "altitude"],
+                ["atmosphere", "serene", "majestic", "sense", "tranquility"],
+            ],
+        },
+        "Phi-4-multimodal-instruct": {
+            "image": [
+                ["depicts", "landscape", "mountain", "half", "dome"],
+                ["atmosphere", "serene", "sense", "tranquility", "peace."],
+            ],
+        },
+    }
+    # Build command for image modality
+    cmd = [
+        str(example_root / "quickstart_multimodal.py"),
+        "--model_dir",
+        f"{llm_models_root()}/{model_path}",
+        "--modality",
+        "image",
+        "--multiturn",
+        "--prompt",
+        *accuracy_inputs["image"]["prompt"],
+        "--media",
+        *accuracy_inputs["image"]["media"],
+    ]
+
+    # Add model-specific configurations
+    if model_name == "gemma-3-27b-it":
+        # Gemma3 VLM needs a custom mask which is only supported by flashinfer backend currently.
+        # Custom mask involves bidirectional masking of image tokens in context phase. To get this
+        # correct, chunked prefill and kv cache reuse need to be turned off.
+        cmd.append("--image_format=pil")
+        cmd.append("--attention_backend=FLASHINFER")
+        cmd.append("--disable_kv_cache_reuse")
+    elif model_name == "Phi-4-multimodal-instruct":
+        # Set max_seq_len to 4096 to use short rope factor.
+        cmd.append("--max_seq_len=4096")
+        cmd.append("--load_lora")
+        cmd.append("--auto_model_name")
+        cmd.append("Phi4MMForCausalLM")
+
+    output = llm_venv.run_cmd(cmd, caller=check_output)
+    print("output:", output)
+    # Set match ratio based on model
+    match_ratio = 4.0 / 5
+    if model_name == "Phi-4-multimodal-instruct":
+        match_ratio = 0.6
+
+    # Check output accuracy
+    for prompt_output, prompt_keywords in zip(
+            parse_output(output), expected_keywords[model_name]["image"]):
+        matches = [
+            keyword in prompt_output.lower() for keyword in prompt_keywords
+        ]
+        obs_match_ratio = 1. * sum(matches) / len(matches)
+        print("prompt_output:", prompt_output)
+        print("prompt_keywords:", prompt_keywords)
+        print("matches:", matches)
+        print("obs_match_ratio:", obs_match_ratio)
+        assert obs_match_ratio >= match_ratio, f"Incorrect output!\nGenerated \"{prompt_output}\"\nExpected keywords \"{prompt_keywords}\"\n Matched keywords: {matches}\n Observed match ratio {obs_match_ratio} below threshold {match_ratio}"
+
+    print("All answers are correct!")
+
+
 @pytest.mark.parametrize("model_name,model_path", [
     ("BertForSequenceClassification", "bert/bert-base-uncased-yelp-polarity"),
 ])

tests/integration/test_lists/qa/llm_function_full.txt

@@ -602,6 +602,9 @@ test_e2e.py::test_ptp_quickstart_multimodal_phi4mm[image_audio]
 test_e2e.py::test_ptp_quickstart_multimodal_2gpu[gemma-3-27b-it-gemma/gemma-3-27b-it]
 test_e2e.py::test_ptp_quickstart_multimodal_2gpu[mistral-small-3.1-24b-instruct-Mistral-Small-3.1-24B-Instruct-2503]
 test_e2e.py::test_ptp_quickstart_multimodal_2gpu[Phi-4-multimodal-instruct-multimodals/Phi-4-multimodal-instruct]
+test_e2e.py::test_ptp_quickstart_multimodal_multiturn[gemma-3-27b-it-gemma/gemma-3-27b-it]
+test_e2e.py::test_ptp_quickstart_multimodal_multiturn[mistral-small-3.1-24b-instruct-Mistral-Small-3.1-24B-Instruct-2503]
+test_e2e.py::test_ptp_quickstart_multimodal_multiturn[Phi-4-multimodal-instruct-multimodals/Phi-4-multimodal-instruct]
 test_e2e.py::test_ptp_quickstart_bert[VANILLA-BertForSequenceClassification-bert/bert-base-uncased-yelp-polarity]
 test_e2e.py::test_ptp_quickstart_bert[TRTLLM-BertForSequenceClassification-bert/bert-base-uncased-yelp-polarity]
 test_e2e.py::test_ptp_star_attention_example[Llama3.1-8B-BF16-llama-3.1-model/Meta-Llama-3.1-8B]