[Performance] Speed up subgraph tracing - add direct dict lookup for O(1) node membership checks - #2999
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📝 WalkthroughWalkthroughThe change updates sequential graph partitioning, adds tests for partition execution and ordering, and introduces a configurable ChangesTrace subgraph partitioning
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🔵 Low · up to The tracing change is mergeable with owner awareness, but the benchmark CLI should reject non-positive dimensions instead of failing with division by zero; this is limited to benchmark tooling and does not indicate a production runtime issue. Possibly related PRs
Suggested labels: Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant CLI
participant TraceTargetModel
participant trace_subgraphs
participant validate_subgraphs
participant ResultLogger
CLI->>TraceTargetModel: build configurable model
CLI->>trace_subgraphs: match targets and trace subgraphs
trace_subgraphs-->>validate_subgraphs: return target matches and subgraphs
validate_subgraphs-->>CLI: validate counts and graph structure
CLI->>ResultLogger: record timing, RSS, and summary output
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✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
🧪 Generate unit tests (beta)
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Code Review
This pull request introduces a synthetic benchmark and unit tests for trace_subgraphs and optimizes the topological partitioning algorithm by replacing linear searches with set membership checks. The reviewer suggests further optimizing the partition lookup from O(P) to O(1) by replacing the list of sets with a single flat dictionary mapping nodes directly to their partition indices, eliminating nested loops.
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| partitions: list[list[Node]] = [[]] | ||
| partition_sets: list[set[Node]] = [set()] |
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Instead of maintaining a list of sets (partition_sets) which requires a sequential scan of node_to_partition: dict[Node, int] to map each node directly to its partition index. This reduces the lookup time from
| partition_sets: list[set[Node]] = [set()] | |
| node_to_partition: dict[Node, int] = {} |
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This is not a bad suggestion
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Hi Kyle! Yeahp, let me undraft this the soonest. 🙏🏻
| partitions[partition_index].append(node) | ||
| partition_sets[partition_index].add(node) |
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Update the partition assignment to record the node's partition index in our node_to_partition dictionary.
| partitions[partition_index].append(node) | |
| partition_sets[partition_index].add(node) | |
| partitions[partition_index].append(node) | |
| node_to_partition[node] = partition_index |
| user_partitions = [] | ||
| for user in node.users: | ||
| for index in range(len(partitions)): | ||
| if user in partitions[index]: | ||
| if user in partition_sets[index]: | ||
| user_partitions.append(index) | ||
| break | ||
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| # workaround | ||
| if len(user_partitions): | ||
| partition_index = min(user_partitions) | ||
| partitions[partition_index].insert(0, node) | ||
| partition_sets[partition_index].add(node) |
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With the node_to_partition dictionary, we can now perform a direct
| user_partitions = [] | |
| for user in node.users: | |
| for index in range(len(partitions)): | |
| if user in partitions[index]: | |
| if user in partition_sets[index]: | |
| user_partitions.append(index) | |
| break | |
| # workaround | |
| if len(user_partitions): | |
| partition_index = min(user_partitions) | |
| partitions[partition_index].insert(0, node) | |
| partition_sets[partition_index].add(node) | |
| user_partitions = [] | |
| for user in node.users: | |
| if user in node_to_partition: | |
| user_partitions.append(node_to_partition[user]) | |
| # workaround | |
| if len(user_partitions): | |
| partition_index = min(user_partitions) | |
| partitions[partition_index].insert(0, node) | |
| node_to_partition[node] = partition_index |
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Signed-off-by: wanadzhar913 <adzhar.faiq@gmail.com>
Signed-off-by: wanadzhar913 <adzhar.faiq@gmail.com>
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Signed-off-by: wanadzhar913 <adzhar.faiq@gmail.com>
…many consumers, assert it in validation, and update benchmark timings. Signed-off-by: wanadzhar913 <adzhar.faiq@gmail.com>
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Actionable comments posted: 2
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Inline comments:
In `@benchmarks/trace_subgraphs/benchmark.py`:
- Around line 87-123: Validate --num-targets and --targets-per-subgraph in
parse_args before model construction or tracing, rejecting values less than one
with an argparse error. Keep valid positive dimensions unchanged and ensure zero
or negative inputs cannot reach the validation logic that divides by the
grouping value.
In `@tests/llmcompressor/pipelines/sequential/test_helpers.py`:
- Around line 117-126: The test_topological_partition_coverage fixture lacks an
actual get_attr node, making _assert_get_attr_precedes_consumers ineffective.
Add a model fixture with a shared registered buffer or parameter consumed across
partition boundaries, then update the test to assert the get_attr node’s
partition placement and verify partitioned execution matches the original
model’s forward output.
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📒 Files selected for processing (4)
benchmarks/trace_subgraphs/README.mdbenchmarks/trace_subgraphs/benchmark.pysrc/llmcompressor/pipelines/sequential/helpers.pytests/llmcompressor/pipelines/sequential/test_helpers.py
🔗 Linked repositories identified
CodeRabbit considers these linked repositories for cross-repo context during reviews:
vllm-project/compressed-tensors(manual)
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| # create subgraphs | ||
| for partition_nodes in partitions: | ||
| partition_set = set(partition_nodes) |
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Would it be better to have partitions just be a partitions: list[set[Node]] from the beginning?
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Oh, I wanted the partitions (from topological_partition) to stay topologically ordered, so I maintained the list data structure. partition_graph relies on that ordering, since node_copy resolves each node's in-partition inputs through node_map, which requires producers to be copied first.
partition_set = set(partition_nodes)'s mainly there for O(1) membership checks.
2 participants
SUMMARY
get_attrnode with many consumers to exercise delayed partition placement.ISSUE
BENCHMARKS
The benchmark was run on a WSL2 with Python 3.13.4 and PyTorch 2.12.1 on CPU.
The workload contains 100,000 matched targets grouped into 335 subgraphs with up to 300 targets each. The synthetic model reads a shared model buffer each layer, producing one shared
get_attrnode with many consumers. Grouping targets creates sufficiently large partitions to exercise the membership-check optimization without constructing 100,001 single-target subgraphs.Only
trace_subgraphswas timed. Results are medians of three runs.2d7a7ea0)This provides a 16.5x speedup on the benchmark workload. The baseline regresses heavily on the
get_attrcase because it repeatedly scans partition lists to locate each consumer; this PR avoids that withnode_to_partitionlookups.The baseline was measured by exporting commit
2d7a7ea058793447faa40b75d285c7ce2111c11fto an isolated directory and pointingPYTHONPATHat itssrc/tree. The complete workload and reproduction instructions are documented inbenchmarks/trace_subgraphs/README.md.TEST PLAN
python3 -m pytest -q tests/llmcompressor/pipelines/sequential/ CUDA_VISIBLE_DEVICES="" python benchmarks/trace_subgraphs/benchmark.py --smokeADDITIONAL
Happy to standardize on benchmarks by #2992 since those came first.