[None][feat] Wire Rubin locality-domain execution through PyTorch models

[peaceh-nv]

Add opt-in enable_locality_domains plumbing from LlmArgs and serve through ModelLoader to Linear, GatedMLP, MLA, and DeepSeek V3/V3.2/V4 callers. Register BF16 locality-domain GEMM/BMM and non-inplace Rubin NVFP4 GEMM; write partition results directly into the corresponding output slices.

Adapt Linear, MLA, and CuTe DSL MoE weight localization to staged loading, full-weight release, and reload. Guard incompatible fused allreduce and weight-sharing paths. Restore skipped LD tests and cover numerical correctness, CUDA graphs, lifecycle, routing, and API stability.

Dev Engineer Review

The change adds opt-in locality-domain execution from TorchLlmArgs and ModelLoader through Linear, GatedMLP, MLA, DeepSeek V3/V4, and CuTe DSL MoE components.

It adds Rubin BF16 GEMM/BMM and non-inplace NVFP4 GEMM operators. It also adds validation, partition-aware output handling, and localized-weight release, reload, and rebuild flows. GMS, fused allreduce, post-transform, and weight-sharing incompatibilities are guarded.

Key risks include hardware-specific dispatch, output aliasing, weight lifecycle correctness, fused-path regressions, and API compatibility. Review finding counts and test execution results are unavailable.

QA Engineer Review

Tests cover API stability, default-off behavior, GMS rejection, post-transform qualification, Rubin BF16 and NVFP4 correctness, CUDA graphs, tactic selection, device routing, Linear, GatedMLP, MLA, DeepSeek, and MoE lifecycle behavior. Several previously skipped locality-domain tests are now enabled.

No files under tests/integration/test_lists/ changed. Coverage is needs follow-up because execution results and supported Rubin hardware validation are unavailable.

Per-File QA Perspective

• tensorrt_llm/_torch/attention/backends/sparse/deepseek_v4/module.py: Verify Q projection fallback when locality-domain shards exist.
• tensorrt_llm/_torch/attention/mla.py: Verify locality wiring, BMM dispatch, shard rebuilding, rebinding, and reload.
• tensorrt_llm/_torch/compilation/utils.py: Verify mutation metadata for graph handling.
• tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py: Verify Rubin validation, partition selection, fake implementations, and output behavior.
• tensorrt_llm/_torch/models/modeling_deepseekv3.py: Verify locality routing and unpartitioned shared experts.
• tensorrt_llm/_torch/models/modeling_deepseekv4.py: Verify attention enablement and absorption-weight rebinding.
• tensorrt_llm/_torch/modules/gated_mlp.py: Verify locality propagation and SwiGLU fallback.
• tensorrt_llm/_torch/modules/linear.py: Verify sharding, release, reload, quantization guards, and disabled-mode behavior.
• tensorrt_llm/_torch/moe/fused_moe/fused_moe_cute_dsl.py: Verify idempotent transforms and repeated reloads.
• tensorrt_llm/_torch/pyexecutor/model_loader.py: Verify policy propagation and incompatible configuration rejection.
• tensorrt_llm/_torch/weight_sharing/post_transform_profiles.py: Verify localized-weight profile qualification.
• tensorrt_llm/commands/serve.py: Verify the default-off CLI option and propagation.
• tensorrt_llm/llmapi/llm_args.py: Verify field placement, defaults, and GMS validation.
• tensorrt_llm/usage/llm_args_golden_manifest.json: Verify manifest schema and regeneration.
• tensorrt_llm/_torch/models/modeling_utils.py: Verify excluded modules retain intended quantization behavior.
• tests/unittest/_torch/attention/test_mla_locality_domain.py: Covers MLA wiring, shard rebuilding, lifecycle, and BMM dispatch. No integration test-list change was made.
• tests/unittest/_torch/executor/test_model_loader_gms.py: Covers early GMS rejection. No integration test-list change was made.
• tests/unittest/_torch/executor/test_model_loader_mx.py: Covers post-transform fallback. No integration test-list change was made.
• tests/unittest/_torch/modules/test_gated_mlp.py: Covers locality propagation and fused SwiGLU eligibility. No integration test-list change was made.
• tests/unittest/_torch/multi_gpu/test_linear.py: Covers BF16/FP4 allreduce, released weights, routing, and accuracy. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py: Adds Rubin execution and validation helpers. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/test_cute_dsl_bf16_dense_locality_domain_rubin.py: Covers Rubin GEMM/BMM correctness, graphs, tactics, routing, and model paths. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py: Covers MoE lifecycle and reload behavior. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/test_dense_gemm_act_fusion.py: Enables NVFP4 SwiGLU capability coverage. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/test_fp4_linear.py: Covers FP4 locality execution, reloads, bias, SwiGLU, non-inplace execution, and graphs. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/test_locality_domain_planner.py: Enables planner and policy tests. No integration test-list change was made.
• tests/unittest/_torch/thop/parallel/test_locality_domain_utils.py: Enables occupancy and Linear routing tests. No integration test-list change was made.
• tests/unittest/api_stability/references/llm.yaml: Adds the prototype API flag. Verify API stability checks.
• tests/unittest/api_stability/references/trtllm_serve_cli.yaml: Adds the prototype CLI flag. Verify CLI stability checks.
• tests/unittest/llmapi/test_llm_args.py: Covers defaults, enablement, and GMS validation. No integration test-list change was made.
• tests/unittest/_torch/modeling/test_modeling_deepseekv3.py: Covers quantization, fused selection, and shared-expert behavior. No integration test-list change was made.
• tests/unittest/_torch/modeling/test_modeling_deepseekv4.py: Covers absorption-weight rebinding and transformation state. No integration test-list change was made.

Description

Test Coverage

PR Checklist

Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

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Walkthrough

Locality-domain execution now spans serving configuration, model loading, BF16 and NVFP4 kernels, DeepSeek and MLA integration, MoE weight handling, and CPU, GPU, and distributed tests.

Changes

Locality-domain execution

Layer / File(s)	Summary
Configuration and model-loading wiring tensorrt_llm/commands/serve.py, tensorrt_llm/llmapi/llm_args.py, tensorrt_llm/_torch/pyexecutor/..., tests/unittest/api_stability/..., tests/unittest/llmapi/..., tests/unittest/_torch/executor/...	Adds the enable_locality_domains serving option, propagates LocalityDomainPolicy, qualifies localized-weight profiles, and rejects incompatible GMS loading.
Partitioned linear execution and kernels tensorrt_llm/_torch/modules/linear.py, tensorrt_llm/_torch/custom_ops/..., tensorrt_llm/_torch/compilation/utils.py	Adds partition-aware BF16 and NVFP4 operators, validation, fake registrations, and mutation metadata.
Attention, MLP, and MoE integration tensorrt_llm/_torch/attention/..., tensorrt_llm/_torch/models/modeling_deepseek*.py, tensorrt_llm/_torch/modules/gated_mlp.py, tensorrt_llm/_torch/moe/...	Propagates locality settings through DeepSeek, MLA, gated MLP, and MoE components. MLA builds localized absorption shards, while MoE uses transform and reload hooks.
Component and lifecycle validation tests/unittest/_torch/attention/..., tests/unittest/_torch/modules/..., tests/unittest/_torch/modeling/..., tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py	Tests projection wiring, shard transformation, unsupported configurations, fused SwiGLU behavior, DeepSeek behavior, loader restrictions, and MoE reload handling.
Rubin, FP4, and distributed validation tests/unittest/_torch/thop/parallel/..., tests/unittest/_torch/multi_gpu/test_linear.py	Adds BF16, NVFP4, CUDA graph, autotuning, split-K, multi-device, reload, planner, and distributed execution coverage.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~60 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant ServeCLI
  participant ModelLoader
  participant Linear
  participant RubinCuTeDSL
  ServeCLI->>ModelLoader: pass enable_locality_domains
  ModelLoader->>Linear: pass LocalityDomainPolicy
  Linear->>Linear: partition and release localized weights
  Linear->>RubinCuTeDSL: execute partitioned BF16 or NVFP4 operation
  RubinCuTeDSL-->>Linear: write localized output shard

Loading

Suggested reviewers: juney-nvidia

Merge Risk: 🔵 Low · up to 29fe2

The opt-in locality-domain path is implemented, but regressions in DeepSeek dispatch or small-shape BF16 partition accuracy could escape the current tests. Tightening these checks is advisable before relying on the new execution path broadly.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 22.97% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 148 functions across 26 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the main change: wiring Rubin locality-domain execution through PyTorch models. It follows the required [None][feat] format.
Description check	✅ Passed	The description explains the implementation scope and states the relevant test coverage, including numerical correctness, CUDA graphs, lifecycle, routing, and API stability. It also includes the requi…
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 3

🧹 Nitpick comments (1)

tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py (1)

173-173: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Derive the BF16 accuracy bounds from the reduction length. Both new BF16 locality-domain checks compare against an fp32 reference with a fixed absolute tolerance that is about an order of magnitude above the expected deviation (~4e-3 * sqrt(K)), and rtol=1e-2 only dominates at the large production shapes. At the small shapes a partition that writes a subtly wrong slice still passes.

• tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py#L173-L173: accept rtol/atol as keyword parameters of run_locality_domain_composite, or derive atol from the reduction length, and keep the loose bound only for the large production shapes.
• tests/unittest/_torch/multi_gpu/test_linear.py#L574-L574: replace atol=2.0 with a bound near the expected error for hidden_size=512, for example 0.5.

As per path instructions: "For numerical tests, require tolerances appropriate to dtype, quantization mode, backend, and expected numerical variance. Flag tolerances broad enough to hide material regressions."

🤖 Prompt for AI Agents

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In `@tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py` at
line 173, Derive BF16 comparison tolerances from the reduction length in
run_locality_domain_composite, or accept per-call rtol/atol keywords, keeping
the looser bound only for large production shapes; ensure small-shape checks
cannot hide incorrect partitions. In
tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py lines
173-173, update the output_partition versus expected comparison accordingly. In
tests/unittest/_torch/multi_gpu/test_linear.py lines 574-574, replace atol=2.0
with a tighter bound appropriate for hidden_size=512, such as 0.5.

Source: Path instructions

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Inline comments:
In `@tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py`:
- Line 3659: Parameterize the test’s comparison tolerances alongside quant_algo
in the existing pytest parameterization, using tight BF16 tolerances and the
looser NVFP4 tolerances with clear IDs. Update both torch.testing.assert_close
calls in the partitioned-execution test to consume those parameterized rtol and
atol values instead of fixed shared tolerances.

In `@tests/unittest/_torch/thop/parallel/test_fp4_linear.py`:
- Line 1047: Replace the always-true seq_len condition in the test with an
explicit assertion that seq_len is one of the supported values, so unsupported
shapes fail rather than skipping tactic capture, replay, and accuracy checks.

In `@tests/unittest/llmapi/test_llm_args.py`:
- Around line 3093-3098: Extend test_serve_enable_locality_domain_flag to call
get_llm_args without enable_locality_domains and assert
llm_args.get("enable_locality_domains", False) is False, while preserving the
existing assertion that explicitly enabling the option returns True.

---

Nitpick comments:
In `@tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py`:
- Line 173: Derive BF16 comparison tolerances from the reduction length in
run_locality_domain_composite, or accept per-call rtol/atol keywords, keeping
the looser bound only for large production shapes; ensure small-shape checks
cannot hide incorrect partitions. In
tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py lines
173-173, update the output_partition versus expected comparison accordingly. In
tests/unittest/_torch/multi_gpu/test_linear.py lines 574-574, replace atol=2.0
with a tighter bound appropriate for hidden_size=512, such as 0.5.

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📥 Commits

Reviewing files that changed from the base of the PR and between 58b5c92 and dbb93ed.

📒 Files selected for processing (29)

• tensorrt_llm/_torch/attention/backends/sparse/deepseek_v4/module.py
• tensorrt_llm/_torch/attention/mla.py
• tensorrt_llm/_torch/compilation/utils.py
• tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py
• tensorrt_llm/_torch/models/modeling_deepseekv3.py
• tensorrt_llm/_torch/models/modeling_deepseekv4.py
• tensorrt_llm/_torch/modules/gated_mlp.py
• tensorrt_llm/_torch/modules/linear.py
• tensorrt_llm/_torch/moe/fused_moe/fused_moe_cute_dsl.py
• tensorrt_llm/_torch/pyexecutor/model_loader.py
• tensorrt_llm/_torch/weight_sharing/post_transform_profiles.py
• tensorrt_llm/commands/serve.py
• tensorrt_llm/llmapi/llm_args.py
• tensorrt_llm/usage/llm_args_golden_manifest.json
• tests/unittest/_torch/attention/test_mla_locality_domain.py
• tests/unittest/_torch/executor/test_model_loader_gms.py
• tests/unittest/_torch/executor/test_model_loader_mx.py
• tests/unittest/_torch/modules/test_gated_mlp.py
• tests/unittest/_torch/multi_gpu/test_linear.py
• tests/unittest/_torch/thop/parallel/_cute_dsl_bf16_rubin_test_utils.py
• tests/unittest/_torch/thop/parallel/test_cute_dsl_bf16_dense_locality_domain_rubin.py
• tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py
• tests/unittest/_torch/thop/parallel/test_dense_gemm_act_fusion.py
• tests/unittest/_torch/thop/parallel/test_fp4_linear.py
• tests/unittest/_torch/thop/parallel/test_locality_domain_planner.py
• tests/unittest/_torch/thop/parallel/test_locality_domain_utils.py
• tests/unittest/api_stability/references/llm.yaml
• tests/unittest/api_stability/references/trtllm_serve_cli.yaml
• tests/unittest/llmapi/test_llm_args.py

💤 Files with no reviewable changes (2)

• tests/unittest/_torch/thop/parallel/test_locality_domain_utils.py
• tests/unittest/_torch/thop/parallel/test_locality_domain_planner.py

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Attention part LGTM

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LGTM

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