[None][test] Add accuracy evaluation for AutoDeploy

tensorrt_llm/_torch/auto_deploy/llm_args.py

@@ -3,9 +3,11 @@
 from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
-from pydantic import Field, ValidationInfo, field_validator, model_validator
+from pydantic import Field, PrivateAttr, ValidationInfo, field_validator, model_validator
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
+from tensorrt_llm.models.modeling_utils import QuantConfig
+
 from ...llmapi.llm_args import BaseLlmArgs, BuildConfig, _ParallelConfig
 from ...llmapi.utils import get_type_repr
 from .models import ModelFactory, ModelFactoryRegistry
@@ -259,6 +261,18 @@ class LlmArgs(AutoDeployConfig, BaseLlmArgs, BaseSettings):
     )
     garbage_collection_gen0_threshold: int = Field(default=20000, description="See TorchLlmArgs.")
 
+    _quant_config: Optional[QuantConfig] = PrivateAttr(default=None)
+
+    @property
+    def quant_config(self) -> QuantConfig:
+        if self._quant_config is None:
+            self._quant_config = QuantConfig()
+        return self._quant_config
+
+    @quant_config.setter
+    def quant_config(self, value: QuantConfig):
+        self._quant_config = value
+
     ### VALIDATION #################################################################################
     @field_validator("build_config", mode="before")
     @classmethod

tests/integration/defs/accuracy/accuracy_core.py

@@ -25,6 +25,7 @@
 import tensorrt_llm.evaluate
 from tensorrt_llm import LLM as PyTorchLLM
 from tensorrt_llm._tensorrt_engine import LLM
+from tensorrt_llm._torch.auto_deploy import LLM as AutoDeployLLM
 from tensorrt_llm.builder import BuildConfig
 from tensorrt_llm.llmapi import SamplingParams
 from tensorrt_llm.llmapi.llm_args import DecodingBaseConfig
@@ -144,7 +145,7 @@ def get_num_samples_and_threshold(self, **acc_specs):
         return num_samples, threshold
 
     def evaluate(self,
-                 llm: Union[LLM, PyTorchLLM],
+                 llm: Union[LLM, PyTorchLLM, AutoDeployLLM],
                  extra_acc_spec: Optional[str] = None,
                  extra_evaluator_kwargs: Optional[dict] = None,
                  sampling_params: Optional[SamplingParams] = None,

tests/integration/defs/accuracy/test_llm_api_autodeploy.py

@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+from tensorrt_llm._torch.auto_deploy import LLM as AutoDeployLLM
+from tensorrt_llm.sampling_params import SamplingParams
+
+from ..conftest import llm_models_root
+from .accuracy_core import MMLU, CnnDailymail, LlmapiAccuracyTestHarness
+
+
+class TestLlama3_1_8B(LlmapiAccuracyTestHarness):
+    MODEL_NAME = "meta-llama/Llama-3.1-8B"
+    MODEL_PATH = f"{llm_models_root()}/llama-3.1-model/Meta-Llama-3.1-8B"
+
+    def get_default_kwargs(self):
+        return {
+            'skip_tokenizer_init': False,
+            'trust_remote_code': True,
+            'kv_cache_config': {
+                'enable_block_reuse': False,
+            },
+            'max_batch_size': 512,
+            # 131072 is the max seq len for the model
+            'max_seq_len': 8192,
+            # max num tokens is derived in the build_config, which is not used by AutoDeploy llmargs.
+            # Set it explicitly here to 8192 which is the default in build_config.
+            'max_num_tokens': 8192,
+            'skip_loading_weights': False,
+            'compile_backend': 'torch-opt',
+            'free_mem_ratio': 0.7,
+            'cuda_graph_batch_sizes': [1, 2, 4, 8, 16, 32, 64, 128, 256]
+        }
+
+    def get_default_sampling_params(self):
+        eos_id = -1
+        beam_width = 1
+        return SamplingParams(end_id=eos_id,
+                              pad_id=eos_id,
+                              n=beam_width,
+                              use_beam_search=beam_width > 1)
+
+    @pytest.mark.skip_less_device_memory(32000)
+    def test_auto_dtype(self):
+        kwargs = self.get_default_kwargs()
+        sampling_params = self.get_default_sampling_params()
+        with AutoDeployLLM(model=self.MODEL_PATH,
+                           tokenizer=self.MODEL_PATH,
+                           **kwargs) as llm:
+            task = CnnDailymail(self.MODEL_NAME)
+            task.evaluate(llm)
+            task = MMLU(self.MODEL_NAME)
+            task.evaluate(llm, sampling_params=sampling_params)