chore: import upstream snapshot with attribution

samples/trtexec/profiler.py

@@ -0,0 +1,211 @@
+#!/usr/bin/env python3
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2026 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.
+#
+
+"""
+Print a trtexec profile from a JSON file
+
+Given a JSON file containing a trtexec profile,
+this program prints the profile in CSV table format.
+Each row represents a layer in the profile.
+
+The output format can be optionally converted to a
+format suitable for GNUPlot.
+"""
+
+import sys
+import json
+import argparse
+import prn_utils as pu
+
+
+allFeatures = ["name", "timeMs", "averageMs", "percentage"]
+
+defaultFeatures = ",".join(allFeatures)
+
+descriptions = ["layer name", "total layer time", "average layer time", "percentage of total time"]
+
+featuresDescription = pu.combineDescriptions("Features are (times in ms):", allFeatures, descriptions)
+
+
+def hasNames(features):
+    """Check if the name is included in the set"""
+
+    return "name" in features
+
+
+def totalData(features, profile):
+    """Add row at the bottom with the total"""
+
+    accumulator = {}
+    for f in features:
+        accumulator[f] = 0
+    accumulator["name"] = "total"
+
+    for row in profile:
+        for f in features:
+            if f in row and not f == "name":
+                accumulator[f] += row[f]
+
+    return accumulator
+
+
+def findAndRemove(profile, name):
+    """Find named row in profile and remove"""
+
+    for r in range(len(profile)):
+        if profile[r]["name"] == name:
+            row = profile[r]
+            del profile[r]
+            return row
+
+    return None
+
+
+def refName(name):
+    """Add prefix ref to name"""
+
+    return "ref" + name[0].capitalize() + name[1:]
+
+
+def refFeatures(names):
+    """Add prefix ref to features names"""
+
+    refNames = []
+    for name in names:
+        refNames.append(refName(name))
+    return refNames
+
+
+def mergeHeaders(features, skipFirst=True):
+    """Duplicate feature names for reference and target profile"""
+
+    if skipFirst:
+        return [features[0]] + refFeatures(features[1:]) + features[1:] + ["% difference"]
+    return refFeatures(features) + features + ["% difference"]
+
+
+def addReference(row, reference):
+    """Add reference results to results dictionary"""
+
+    for k, v in reference.items():
+        if k == "name":
+            if k in row:
+                continue
+        else:
+            k = refName(k)
+        row[k] = v
+
+
+def mergeRow(reference, profile, diff):
+    """Merge reference and target profile results into a single row"""
+
+    row = {}
+    if profile:
+        row = profile
+    if reference:
+        addReference(row, reference)
+    if diff:
+        row["% difference"] = diff
+
+    return row
+
+
+def alignData(reference, profile, threshold):
+    """Align and merge reference and target profiles"""
+
+    alignedData = []
+    for ref in reference:
+        prof = findAndRemove(profile, ref["name"])
+
+        if prof:
+            diff = (prof["averageMs"] / ref["averageMs"] - 1) * 100
+            if abs(diff) >= threshold:
+                alignedData.append(mergeRow(ref, prof, diff))
+        else:
+            alignedData.append(mergeRow(ref, None, None))
+
+    for prof in profile:
+        alignedData.append(mergeRow(None, prof, None))
+
+    return alignedData
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--features",
+        metavar="F[,F]*",
+        default=defaultFeatures,
+        help="Comma separated list of features to print. " + featuresDescription,
+    )
+    parser.add_argument("--total", action="store_true", help="Add total time row.")
+    parser.add_argument("--gp", action="store_true", help="Print GNUPlot format.")
+    parser.add_argument("--no-header", action="store_true", help="Omit the header row.")
+    parser.add_argument("--threshold", metavar="T", default=0.0, type=float, help="Threshold of percentage difference.")
+    parser.add_argument("--reference", metavar="R", help="Reference profile file name.")
+    parser.add_argument("name", metavar="filename", help="Profile file.")
+    args = parser.parse_args()
+
+    global allFeatures
+    features = args.features.split(",")
+    for f in features:
+        if not f in allFeatures:
+            print("Feature {} not recognized".format(f))
+            return
+
+    count = args.gp and not hasNames(features)
+
+    profile = None
+    reference = None
+    referenceCount = 0
+
+    with open(args.name) as f:
+        profile = json.load(f)
+        profileCount = profile[0]["count"]
+        profile = profile[1:]
+
+    if args.reference:
+        with open(args.reference) as f:
+            reference = json.load(f)
+            referenceCount = reference[0]["count"]
+            reference = reference[1:]
+        allFeatures = mergeHeaders(allFeatures)
+        features = mergeHeaders(features, hasNames(features))
+
+    if not args.no_header:
+        if reference:
+            comment = "#" if args.gp else ""
+            print(comment + "reference count: {} - profile count: {}".format(referenceCount, profileCount))
+        pu.printHeader(allFeatures, features, args.gp, count)
+
+    if reference:
+        profile = alignData(reference, profile, args.threshold)
+
+    if args.total:
+        profile.append(totalData(allFeatures, profile))
+        if reference:
+            total = profile[len(profile) - 1]
+            total["% difference"] = (total["averageMs"] / total["refAverageMs"] - 1) * 100
+
+    profile = pu.filterData(profile, allFeatures, features)
+
+    pu.printCsv(profile, count)
+
+
+if __name__ == "__main__":
+    sys.exit(main())