Integrate Automated QDQ placement tool - part 2

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

Author: willg-nv

modelopt/onnx/op_types.py

@@ -303,3 +303,22 @@ def is_data_dependent_shape_op(op_type: str):
         "NonZero",
         "RoiAlign",
     ]
+
+
+def get_symmetric_ops():
+    """Returns set of commutative/symmetric operations where operand order doesn't matter."""
+    return {
+        "Add",
+        "Mul",
+        "And",
+        "Or",
+        "Xor",
+        "Equal",
+        "Max",
+        "Min",
+        "Sum",
+        "Mean",
+        "BitwiseAnd",
+        "BitwiseOr",
+        "BitwiseXor",
+    }

modelopt/onnx/quantization/autotune/__init__.py

@@ -0,0 +1,144 @@
+# 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.
+
+"""Pattern-Based Q/DQ Autotuning for ONNX Models.
+
+This package provides automated optimization of Quantize/Dequantize (Q/DQ) node placement
+in ONNX computation graphs to minimize TensorRT inference latency. It uses pattern-based
+region analysis to efficiently explore and optimize Q/DQ insertion strategies.
+
+**Key Features:**
+
+- **Automated Region Discovery**: Hierarchical decomposition of computation graphs into
+  LEAF and COMPOSITE regions with automatic pattern identification
+
+- **Pattern-Based Optimization**: Groups structurally-similar regions and optimizes them
+  together, making the process efficient and consistent
+
+- **TensorRT Performance Measurement**: Direct integration with TensorRT Python API for
+  accurate latency profiling of each Q/DQ configuration
+
+- **State Management**: Checkpoint/resume capability for long-running optimizations with
+  incremental state saving after each region
+
+- **Pattern Cache**: Warm-start optimization using learned schemes from previous runs,
+  enabling transfer learning across models
+
+**Core Components:**
+
+Autotuner Classes:
+    - QDQAutotuner: Main autotuner with automatic hierarchical region discovery
+    - QDQAutotunerBase: Base class for custom region identification strategies
+
+Region Management:
+    - Region: Hierarchical subgraph representation (nodes + children)
+    - RegionType: Enumeration (LEAF, COMPOSITE, ROOT)
+    - CombinedRegionSearch: Two-phase region discovery (partitioning + refinement)
+    - RegionPattern: Structural pattern analysis and matching for region grouping
+
+Q/DQ Insertion Points:
+    - InsertionScheme: Collection of Q/DQ insertion points for a region pattern
+    - NodeInputInsertionPoint: Q/DQ insertion at specific node inputs
+    - ChildRegionInputInsertionPoint: Q/DQ insertion at child region input boundaries
+    - RegionOutputInsertionPoint: Q/DQ insertion at region output boundaries
+
+Configuration & State:
+    - Config: Autotuning parameters (quant type, thresholds, verbosity)
+    - PatternCache: Top-performing schemes indexed by pattern (warm-start)
+    - PatternSchemes: Scheme collection and measurement results for a pattern
+
+Benchmarking:
+    - Benchmark: Abstract base class for model benchmarking
+    - TensorRTPyBenchmark: Benchmark using TensorRT Python API (recommended)
+    - TrtExecBenchmark: Benchmark using trtexec command-line tool (legacy)
+
+**Quick Start:**
+
+    >>> from modelopt.onnx.quantization.autotune import QDQAutotuner, Config
+    >>> import onnx
+    >>> # Load model and initialize autotuner
+    >>> model = onnx.load("model.onnx")
+    >>> autotuner = QDQAutotuner(model)
+    >>> # Configure autotuning parameters
+    >>> config = Config(default_quant_type="int8")
+    >>> autotuner.initialize(config)
+    >>> # Generate and test Q/DQ schemes
+    >>> # (see workflows.region_pattern_autotuning_workflow for complete example)
+
+**Command-Line Interface:**
+
+    The package can be run directly as a module:
+
+    $ python -m modelopt.onnx.quantization.autotune --model model.onnx --output ./output
+    $ python -m modelopt.onnx.quantization.autotune --model model.onnx --quant-type fp8
+
+**See Also:**
+
+    - workflows.region_pattern_autotuning_workflow: Complete end-to-end optimization
+    - QDQAutotuner: Main autotuner class documentation
+    - RegionPattern: Pattern matching and signature computation
+"""
+
+# Core data structures
+from .common import (
+    AutotunerError,
+    AutotunerNotInitializedError,
+    Config,
+    InsertionScheme,
+    InvalidSchemeError,
+    PatternCache,
+    PatternSchemes,
+    Region,
+    RegionError,
+    RegionType,
+)
+
+# Insertion points (from dedicated module)
+from .insertion_points import (
+    ChildRegionInputInsertionPoint,
+    NodeInputInsertionPoint,
+    RegionOutputInsertionPoint,
+    ResolvedInsertionPoint,
+)
+
+# Pattern analysis
+from .region_pattern import RegionPattern
+
+# Region search
+from .region_search import CombinedRegionSearch
+
+# Public API
+__all__ = [
+    # Exceptions
+    "AutotunerError",
+    "AutotunerNotInitializedError",
+    "ChildRegionInputInsertionPoint",
+    "CombinedRegionSearch",
+    # Configuration and state
+    "Config",
+    # Q/DQ insertion
+    "InsertionScheme",
+    "InvalidSchemeError",
+    "NodeInputInsertionPoint",
+    "ResolvedInsertionPoint",
+    "PatternCache",
+    "PatternSchemes",
+    # Region classes
+    "Region",
+    "RegionError",
+    "RegionOutputInsertionPoint",
+    "RegionPattern",
+    "RegionType",
+]