feat: convert profiler perf data for mocker to use (#4370)

Signed-off-by: hongkuanz <hongkuanz@nvidia.com>

components/src/dynamo/mocker/utils/planner_profiler_perf_data_converter.py

+#  SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#  SPDX-License-Identifier: Apache-2.0
+
+"""
+This script converts planner profiler's results for mocker to use.
+
+Example prefill query:
+    input:
+        isl: 3000
+
+    1. binary search prefill_isl to find isl_idx
+    2. predicted TTFT is prefill_ttft_ms[isl_idx]
+For chunked prefill, can ignore the KV cache read time and use ISL=prefill_tokens in this iteration.
+This ignores the KV read time, which might leads to slightly lower latency..
+
+Example decode query:
+    input:
+        active_kv_tokens: 10000
+        batch_size: 100
+
+    1. derive decode_context_length = active_kv_tokens / batch_size = 100
+    2. binary search decode_active_kv_tokens to find kv_idx
+    3. binary search decode_context_length to find context_idx
+    4. predicted ITL is decode_itl[kv_idx, context_idx]
+
+For aggregated engines, can separately query prefill and decode and use their sum as the total latency.
+This ignores the fact that active tokens' up/down projection is usually combine in one kernel,
+and might leads to slightly higher latency.
+"""
+
+import argparse
+import logging
+import os
+
+import numpy as np
+
+from dynamo.planner.utils.perf_interpolation import (
+    DecodeInterpolator,
+    PrefillInterpolator,
+)
+from dynamo.runtime.logging import configure_dynamo_logging
+
+configure_dynamo_logging()
+logger = logging.getLogger(__name__)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--profile_results_dir", type=str, required=True)
+    parser.add_argument("--resolution", type=int, default=100)
+    parser.add_argument("--output_dir", type=str, default="")
+    args = parser.parse_args()
+
+    if not args.output_dir:
+        args.output_dir = args.profile_results_dir
+
+    logger.info(
+        f"Converting profile results from {args.profile_results_dir} to {args.output_dir}..."
+    )
+
+    # first convert prefill
+    prefill_interpolator = PrefillInterpolator(args.profile_results_dir)
+
+    prefill_x = np.linspace(
+        prefill_interpolator.ttft_interpolator.x.min(),
+        prefill_interpolator.ttft_interpolator.x.max(),
+        args.resolution,
+    )
+    prefill_y = prefill_interpolator.ttft_interpolator(prefill_x)
+
+    result = {
+        "prefill_isl": prefill_x.tolist(),
+        "prefill_ttft_ms": prefill_y.tolist(),
+    }
+
+    # then convert decode
+    decode_interpolator = DecodeInterpolator(
+        args.profile_results_dir, resolution=args.resolution
+    )
+
+    decode_active_kv_tokens = decode_interpolator.xi * decode_interpolator.max_kv_tokens
+    decode_context_length = decode_interpolator.yi
+    decode_itl = decode_interpolator.itl_interpolator.transpose()
+
+    result["decode_active_kv_tokens"] = decode_active_kv_tokens.tolist()
+    result["decode_context_length"] = decode_context_length.tolist()
+    result["decode_itl"] = decode_itl.tolist()
+
+    np.savez(os.path.join(args.output_dir, "perf_data.npz"), **result)
+
+    logger.info(f"Wrote perf data to {os.path.join(args.output_dir, 'perf_data.npz')}")

components/src/dynamo/planner/utils/perf_interpolation.py

@@ -247,7 +247,7 @@ if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("--profile_results_dir", type=str, required=True)
+    parser.add_argument("--profile-results-dir", type=str, required=True)
     parser.add_argument("--isl", type=int, default=3000)
     parser.add_argument("--osl", type=int, default=150)
     parser.add_argument("--ttft", type=float, default=100.0, help="in milliseconds")