[TRTLLM-9259][perf] Use torch.compile to fuse copy + layernorm within the LayerNorm module

[chang-l]

Normally, activations are not in float32; as a result, LayerNorm ends up issuing three kernels (2 cp + 1 layernorm op).
This PR leverages torch.compile to fuse these operations into a single kernel, removing the redundant memory traffic and launch overhead.

Dependency PR: #8988

Before this PR:

===========================================================
= PERFORMANCE OVERVIEW
===========================================================
Request Throughput (req/sec):                     1.3717
Total Output Throughput (tokens/sec):             2809.1876
Total Token Throughput (tokens/sec):              4239.6074
Total Latency (ms):                               46658.3300
Average request latency (ms):                     46612.0998
Per User Output Throughput [w/ ctx] (tps/user):   43.9371
Per GPU Output Throughput (tps/gpu):              351.148

With this PR (~1.02x speedup):

===========================================================
= PERFORMANCE OVERVIEW
===========================================================
Request Throughput (req/sec):                     1.4004
Total Output Throughput (tokens/sec):             2867.9297
Total Token Throughput (tokens/sec):              4328.2607
Total Latency (ms):                               45702.6540
Average request latency (ms):                     45658.2250
Per User Output Throughput [w/ ctx] (tps/user):   44.8550
Per GPU Output Throughput (tps/gpu):              358.4912

Summary by CodeRabbit

• Refactor
  • Enhanced layer normalization operations with dynamic compilation optimization to improve performance.

Description

Test Coverage

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[coderabbitai]

📝 Walkthrough

Walkthrough

A decorator (@maybe_compile(dynamic=True)) was added to the forward method of the LayerNorm class to enable dynamic compilation. The import for maybe_compile was added from the utils module. No changes to method signature or core logic.

Changes

Cohort / File(s)	Change Summary
Dynamic compilation for LayerNorm tensorrt_llm/_torch/modules/layer_norm.py	Added import of maybe_compile utility and applied @maybe_compile(dynamic=True) decorator to the forward method to introduce dynamic compilation capability.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~12 minutes

• Verify that maybe_compile utility is correctly imported and available from the specified path
• Confirm that applying dynamic=True to LayerNorm's forward method is the intended behavior and does not introduce unexpected performance or compatibility implications
• Ensure the decorator placement does not inadvertently affect method resolution or override chain behavior

Pre-merge checks and finishing touches

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description check	❓ Inconclusive	The PR description contains performance metrics and background context, but critical template sections (Description, Test Coverage) are not filled in with substantive content.	Fill in the Description section with a clear explanation of the issue and solution, and the Test Coverage section with specific test cases that validate the changes.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically summarizes the main change: using torch.compile with dynamic compilation to optimize LayerNorm by fusing copy and layernorm operations.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[chang-l]

/bot run

[lfr-0531]

LGTM!

[tensorrt-cicd]

PR_Github #24107 [ run ] triggered by Bot. Commit: debe9f2

[tensorrt-cicd]

PR_Github #24107 [ run ] completed with state SUCCESS. Commit: debe9f2
/LLM/main/L0_MergeRequest_PR pipeline #18169 completed with status: 'SUCCESS'
Pipeline passed with automatic retried tests. Check the rerun report for details.