add TestNemotronH_47B and TestNemotronH_56B

Signed-off-by: Xin He (SW-GPU) <[email protected]>

Author: xinhe-nv

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -1588,8 +1588,6 @@ class TestNemotronH(LlmapiAccuracyTestHarness):
 
     @parametrize_with_ids("cuda_graph", [False, True])
     def test_auto_dtype(self, cuda_graph):
-        # TODO: remove max_batch_size after mamba cache manager is supported
-        # ToDo: check 47b and 56b model
         kv_cache_config = KvCacheConfig(enable_block_reuse=False)
         with LLM(self.MODEL_PATH,
                  kv_cache_config=kv_cache_config,
@@ -1615,6 +1613,77 @@ def test_reasoning_fp8_prequantized(self, cuda_graph):
             task.evaluate(llm)
 
 
+@pytest.mark.skip_less_device(8)
+@pytest.mark.skip_less_device_memory(80000)
+class TestNemotronH_47B_Base(LlmapiAccuracyTestHarness):
+    MODEL_NAME = "nvidia/Nemotron-H-47B-Base-8K"
+    MODEL_PATH = f"{llm_models_root()}/Nemotron-H-47B-Base-8K"
+
+    @parametrize_with_ids("cuda_graph", [False, True])
+    @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(8, 1, 1), (8, 1, 4),
+                                                         (8, 1, 8)],
+                             ids=["tp8", "tp8ep4", "tp8ep8"])
+    def test_auto_dtype(self, cuda_graph, tp_size, pp_size, ep_size):
+        kv_cache_config = KvCacheConfig(enable_block_reuse=False)
+        with LLM(self.MODEL_PATH,
+                 tensor_parallel_size=tp_size,
+                 pipeline_parallel_size=pp_size,
+                 moe_expert_parallel_size=ep_size,
+                 kv_cache_config=kv_cache_config,
+                 cuda_graph_config=CudaGraphConfig()
+                 if cuda_graph else None) as llm:
+            task = MMLU(self.MODEL_NAME)
+            task.evaluate(llm)
+            task = GSM8K(self.MODEL_NAME)
+            task.evaluate(llm)
+
+    @skip_pre_hopper
+    @parametrize_with_ids("cuda_graph", [False, True])
+    @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(8, 1, 1), (8, 1, 4),
+                                                         (8, 1, 8)],
+                             ids=["tp8", "tp8ep4", "tp8ep8"])
+    def test_reasoning_fp8_prequantized(self, cuda_graph, tp_size, pp_size,
+                                        ep_size):
+        kv_cache_config = KvCacheConfig(enable_block_reuse=False)
+        with LLM(f"{llm_models_root()}/Nemotron-H-47B-Reasoning-128K-FP8",
+                 kv_cache_config=kv_cache_config,
+                 tensor_parallel_size=tp_size,
+                 pipeline_parallel_size=pp_size,
+                 moe_expert_parallel_size=ep_size,
+                 cuda_graph_config=CudaGraphConfig()
+                 if cuda_graph else None) as llm:
+            assert llm.args.quant_config.quant_algo == QuantAlgo.FP8
+            task = MMLU(self.MODEL_NAME)
+            task.evaluate(llm)
+            task = GSM8K(self.MODEL_NAME)
+            task.evaluate(llm)
+
+
+@pytest.mark.skip_less_device(8)
+@pytest.mark.skip_less_device_memory(80000)
+class TestNemotronH_56B_Base(LlmapiAccuracyTestHarness):
+    MODEL_NAME = "nvidia/Nemotron-H-56B-Base-8K"
+    MODEL_PATH = f"{llm_models_root()}/Nemotron-H-56B-Base-8K"
+
+    @parametrize_with_ids("cuda_graph", [False, True])
+    @pytest.mark.parametrize("tp_size,pp_size,ep_size", [(8, 1, 1), (8, 1, 4),
+                                                         (8, 1, 8)],
+                             ids=["tp8", "tp8ep4", "tp8ep8"])
+    def test_auto_dtype(self, cuda_graph, tp_size, pp_size, ep_size):
+        kv_cache_config = KvCacheConfig(enable_block_reuse=False)
+        with LLM(self.MODEL_PATH,
+                 tensor_parallel_size=tp_size,
+                 pipeline_parallel_size=pp_size,
+                 moe_expert_parallel_size=ep_size,
+                 kv_cache_config=kv_cache_config,
+                 cuda_graph_config=CudaGraphConfig()
+                 if cuda_graph else None) as llm:
+            task = MMLU(self.MODEL_NAME)
+            task.evaluate(llm)
+            task = GSM8K(self.MODEL_NAME)
+            task.evaluate(llm)
+
+
 class TestQwen2_7BInstruct(LlmapiAccuracyTestHarness):
     MODEL_NAME = "Qwen/Qwen2-7B-Instruct"
     MODEL_PATH = f"{llm_models_root()}/Qwen2-7B-Instruct"

tests/integration/test_lists/qa/benchmark_test_list.txt

@@ -15,6 +15,9 @@ accuracy/test_llm_api_pytorch.py::TestLlama3_1NemotronNano8Bv1::test_fp8_prequan
 accuracy/test_llm_api_pytorch.py::TestNemotronH::test_auto_dtype[cuda_graph=True]
 accuracy/test_llm_api_pytorch.py::TestNemotronH::test_auto_dtype[cuda_graph=False]
 accuracy/test_llm_api_pytorch.py::TestNemotronH::test_reasoning_fp8_prequantized[cuda_graph=True]
+accuracy/test_llm_api_pytorch.py::TestNemotronH_47B_Base::test_auto_dtype[tp8ep4-cuda_graph=True]
+accuracy/test_llm_api_pytorch.py::TestNemotronH_47B_Base::test_reasoning_fp8_prequantized[tp8ep8-cuda_graph=True]
+accuracy/test_llm_api_pytorch.py::TestNemotronH_56B_Base::test_auto_dtype[tp8-cuda_graph=True]
 accuracy/test_llm_api_pytorch.py::TestNemotronUltra::test_auto_dtype[tp8ep4-cuda_graph=True]
 accuracy/test_llm_api_pytorch.py::TestNemotronUltra::test_fp8_prequantized[tp8ep4-cuda_graph=True]
 accuracy/test_llm_api_pytorch.py::TestNemotronUltra::test_fp8_prequantized[tp8-cuda_graph=True]