chore: enhance yaml loading arbitrary options in LlmArgs

tensorrt_llm/llmapi/llm_args.py

@@ -1937,15 +1937,9 @@ def update_llm_args_with_extra_dict(
         "quant_config": QuantConfig,
         "calib_config": CalibConfig,
         "build_config": BuildConfig,
-        "kv_cache_config": KvCacheConfig,
         "decoding_config": DecodingConfig,
         "enable_build_cache": BuildCacheConfig,
-        "peft_cache_config": PeftCacheConfig,
-        "scheduler_config": SchedulerConfig,
         "speculative_config": DecodingBaseConfig,
-        "batching_type": BatchingType,
-        "extended_runtime_perf_knob_config": ExtendedRuntimePerfKnobConfig,
-        "cache_transceiver_config": CacheTransceiverConfig,
         "lora_config": LoraConfig,
     }
     for field_name, field_type in field_mapping.items():

tests/unittest/llmapi/test_llm_args.py

@@ -40,27 +40,100 @@ def test_LookaheadDecodingConfig():
     assert pybind_config.max_verification_set_size == 4
 
 
-def test_update_llm_args_with_extra_dict_with_speculative_config():
-    yaml_content = """
+class TestYaml:
+
+    def _yaml_to_dict(self, yaml_content: str) -> dict:
+        with tempfile.NamedTemporaryFile(delete=False) as f:
+            f.write(yaml_content.encode('utf-8'))
+            f.flush()
+            f.seek(0)
+            dict_content = yaml.safe_load(f)
+        return dict_content
+
+    def test_update_llm_args_with_extra_dict_with_speculative_config(self):
+        yaml_content = """
 speculative_config:
-  decoding_type: Lookahead
-  max_window_size: 4
-  max_ngram_size: 3
-  verification_set_size: 4
+    decoding_type: Lookahead
+    max_window_size: 4
+    max_ngram_size: 3
+    verification_set_size: 4
+    """
+        dict_content = self._yaml_to_dict(yaml_content)
+
+        llm_args = TrtLlmArgs(model=llama_model_path)
+        llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
+                                                        dict_content)
+        llm_args = TrtLlmArgs(**llm_args_dict)
+        assert llm_args.speculative_config.max_window_size == 4
+        assert llm_args.speculative_config.max_ngram_size == 3
+        assert llm_args.speculative_config.max_verification_set_size == 4
+
+    def test_llm_args_with_invalid_yaml(self):
+        yaml_content = """
+pytorch_backend_config: # this is deprecated
+    max_num_tokens: 1
+    max_seq_len: 1
 """
-    with tempfile.NamedTemporaryFile(delete=False) as f:
-        f.write(yaml_content.encode('utf-8'))
-        f.flush()
-        f.seek(0)
-        dict_content = yaml.safe_load(f)
-
-    llm_args = TrtLlmArgs(model=llama_model_path)
-    llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
-                                                    dict_content)
-    llm_args = TrtLlmArgs(**llm_args_dict)
-    assert llm_args.speculative_config.max_window_size == 4
-    assert llm_args.speculative_config.max_ngram_size == 3
-    assert llm_args.speculative_config.max_verification_set_size == 4
+        dict_content = self._yaml_to_dict(yaml_content)
+
+        llm_args = TrtLlmArgs(model=llama_model_path)
+        llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
+                                                        dict_content)
+        with pytest.raises(ValueError):
+            llm_args = TrtLlmArgs(**llm_args_dict)
+
+    def test_llm_args_with_build_config(self):
+        # build_config isn't a Pydantic
+        yaml_content = """
+build_config:
+    max_beam_width: 4
+    max_batch_size: 8
+    max_num_tokens: 256
+    """
+        dict_content = self._yaml_to_dict(yaml_content)
+
+        llm_args = TrtLlmArgs(model=llama_model_path)
+        llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
+                                                        dict_content)
+        llm_args = TrtLlmArgs(**llm_args_dict)
+        assert llm_args.build_config.max_beam_width == 4
+        assert llm_args.build_config.max_batch_size == 8
+        assert llm_args.build_config.max_num_tokens == 256
+
+    def test_llm_args_with_kvcache_config(self):
+        yaml_content = """
+kv_cache_config:
+    enable_block_reuse: True
+    max_tokens: 1024
+    max_attention_window: [1024, 1024, 1024]
+    """
+        dict_content = self._yaml_to_dict(yaml_content)
+
+        llm_args = TrtLlmArgs(model=llama_model_path)
+        llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
+                                                        dict_content)
+        llm_args = TrtLlmArgs(**llm_args_dict)
+        assert llm_args.kv_cache_config.enable_block_reuse == True
+        assert llm_args.kv_cache_config.max_tokens == 1024
+        assert llm_args.kv_cache_config.max_attention_window == [
+            1024, 1024, 1024
+        ]
+
+    def test_llm_args_with_pydantic_options(self):
+        yaml_content = """
+max_batch_size: 16
+max_num_tokens: 256
+max_seq_len: 128
+    """
+        dict_content = self._yaml_to_dict(yaml_content)
+
+        llm_args = TrtLlmArgs(model=llama_model_path)
+        llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
+                                                        dict_content)
+        llm_args = TrtLlmArgs(**llm_args_dict)
+        assert llm_args.max_batch_size == 16
+        assert llm_args.max_num_tokens == 256
+        assert llm_args.max_seq_len == 128
 
 
 def check_defaults(py_config_cls, pybind_config_cls):