Integrate Automated QDQ placement tool - part 3

Signed-off-by: Will Guo <[email protected]>

Author: willg-nv

modelopt/onnx/quantization/autotune/__init__.py

@@ -91,6 +91,12 @@
     - RegionPattern: Pattern matching and signature computation
 """
 
+# Autotuner classes
+from .autotuner import QDQAutotuner, QDQAutotunerBase
+
+# Benchmark classes
+from .benchmark import Benchmark, TensorRTPyBenchmark, TrtExecBenchmark
+
 # Core data structures
 from .common import (
     AutotunerError,
@@ -124,21 +130,28 @@
     # Exceptions
     "AutotunerError",
     "AutotunerNotInitializedError",
-    "ChildRegionInputInsertionPoint",
-    "CombinedRegionSearch",
+    # Benchmark classes
+    "Benchmark",
+    "TensorRTPyBenchmark",
+    "TrtExecBenchmark",
     # Configuration and state
     "Config",
     # Q/DQ insertion
     "InsertionScheme",
     "InvalidSchemeError",
     "NodeInputInsertionPoint",
+    "ChildRegionInputInsertionPoint",
+    "RegionOutputInsertionPoint",
     "ResolvedInsertionPoint",
-    "PatternCache",
-    "PatternSchemes",
+    # Main autotuner classes
+    "QDQAutotuner",
+    "QDQAutotunerBase",
     # Region classes
     "Region",
     "RegionError",
-    "RegionOutputInsertionPoint",
     "RegionPattern",
     "RegionType",
+    "PatternCache",
+    "PatternSchemes",
+    "CombinedRegionSearch",
 ]

modelopt/onnx/quantization/autotune/__main__.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024 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.
+
+"""ONNX Q/DQ Autotuning Command-Line Interface.
+
+This module provides a command-line interface for automated Q/DQ (Quantize/Dequantize)
+optimization of ONNX models. It uses pattern-based region analysis and TensorRT performance
+measurement to find optimal Q/DQ insertion points that minimize inference latency.
+
+**Usage Examples:**
+
+    # Basic usage - automatic region discovery and optimization
+    python -m modelopt.onnx.quantization.autotune --model model.onnx
+
+    # INT8 vs FP8 quantization
+    python -m modelopt.onnx.quantization.autotune --model model.onnx --quant-type fp8
+
+    # Warm-start from pattern cache (transfer learning)
+    python -m modelopt.onnx.quantization.autotune \\
+        --model model.onnx \\
+        --pattern-cache ./output/pattern_cache.yaml
+
+    # Import patterns from pre-quantized baseline model
+    python -m modelopt.onnx.quantization.autotune \\
+        --model model.onnx \\
+        --qdq-baseline quantized_baseline.onnx
+
+    # Full example with all optimization options
+    python -m modelopt.onnx.quantization.autotune \\
+        --model model.onnx \\
+        --schemes-per-region 50 \\
+        --pattern-cache pattern_cache.yaml \\
+        --qdq-baseline baseline.onnx \\
+        --output ./results \\
+        --quant-type int8 \\
+        --verbose
+
+    # Use custom TensorRT plugins for model-specific operations
+    python -m modelopt.onnx.quantization.autotune \\
+        --model model.onnx \\
+        --plugin-libraries /path/to/plugin1.so /path/to/plugin2.so
+
+**Output Files:**
+
+    output_dir/
+    ├── autotuner_state.yaml          # Checkpoint for resume capability
+    ├── baseline.onnx                 # Unquantized baseline model
+    ├── optimized_final.onnx          # Final optimized model with Q/DQ
+    ├── logs/                         # TensorRT build logs per scheme
+    │   ├── baseline.log
+    │   ├── region_*_scheme_*.log
+    │   └── final.log
+    └── region_models/                # Best model per region
+        └── region_*_level_*.onnx
+"""
+
+import sys
+
+from modelopt.onnx.quantization.autotune.cli import get_autotune_parser, run_autotune
+
+
+def main():
+    """Command-line entry point for ONNX Q/DQ autotuning.
+
+    Parses command-line arguments and executes the autotuning workflow.
+
+    Returns:
+        Exit code from run_autotune (0 for success, non-zero for errors)
+    """
+    parser = get_autotune_parser()
+    args = parser.parse_args()
+
+    # Run autotuning
+    return run_autotune(args)
+
+
+if __name__ == "__main__":
+    sys.exit(main())