[2/n] Add target-first offline mask reuse calibration

examples/vllm_serve/create_checkpoint_manifest.py

@@ -0,0 +1,43 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 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.
+
+"""Create a deterministic, no-clobber checkpoint manifest for calibration."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from modelopt.torch.sparsity.attention_sparsity.calibration.checkpoint_manifest import (
+    create_checkpoint_manifest,
+)
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(allow_abbrev=False)
+    parser.add_argument("checkpoint", type=Path)
+    parser.add_argument("--model-id", required=True)
+    args = parser.parse_args(argv)
+    try:
+        manifest = create_checkpoint_manifest(args.checkpoint, model=args.model_id)
+    except (OSError, ValueError) as error:
+        parser.error(str(error))
+    print(f"[ModelOpt] Wrote {manifest.manifest_path}")
+    print(f"CHECKPOINT_MANIFEST_SHA256={manifest.sha256}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

modelopt/torch/sparsity/attention_sparsity/calibration/__init__.py

@@ -17,10 +17,54 @@
 
 from .calibrate import calibrate_sparse_attention
 from .calibrator import DynamicThresholdCalibrator
+from .checkpoint_manifest import (
+    CHECKPOINT_MANIFEST_NAME,
+    CheckpointManifestError,
+    StableFileSnapshot,
+    VerifiedCheckpointManifest,
+    create_checkpoint_manifest,
+    read_stable_file_snapshot,
+    stable_file_sha256,
+    verify_checkpoint_manifest,
+)
+from .mask_reuse import (
+    AnchorLayerStats,
+    MaskReuseCalibrationError,
+    MaskReuseObservation,
+    calibrate_mask_reuse_policy,
+    canonical_prefill_threshold_scale_factor,
+    load_mask_reuse_observations,
+    parse_mask_reuse_observations,
+)
+from .mask_reuse_compact import (
+    CompactMaskReuseCapture,
+    CompactMaskReuseCaptureSource,
+    calibrate_compact_mask_reuse_policy,
+    load_compact_mask_reuse_captures,
+)
 from .ruler_dataset import RulerDatasetBuilder
 
 __all__ = [
+    "CHECKPOINT_MANIFEST_NAME",
+    "AnchorLayerStats",
+    "CheckpointManifestError",
+    "CompactMaskReuseCapture",
+    "CompactMaskReuseCaptureSource",
     "DynamicThresholdCalibrator",
+    "MaskReuseCalibrationError",
+    "MaskReuseObservation",
     "RulerDatasetBuilder",
+    "StableFileSnapshot",
+    "VerifiedCheckpointManifest",
+    "calibrate_compact_mask_reuse_policy",
+    "calibrate_mask_reuse_policy",
     "calibrate_sparse_attention",
+    "canonical_prefill_threshold_scale_factor",
+    "create_checkpoint_manifest",
+    "load_compact_mask_reuse_captures",
+    "load_mask_reuse_observations",
+    "parse_mask_reuse_observations",
+    "read_stable_file_snapshot",
+    "stable_file_sha256",
+    "verify_checkpoint_manifest",
 ]