[None][feat] Add DWDP (Distributed Weight Data Parallelism) support for MoE inference by tianyuz-nv · Pull Request #12136 · NVIDIA/TensorRT-LLM

tianyuz-nv · 2026-03-12T03:36:37Z

Summary by CodeRabbit

Release Notes

New Features
- Added DWDP (Double-Weighted Distributed Prefetching) support for disaggregated MoE inference, enabling efficient cross-rank weight prefetching with ping-pong buffering for improved serving performance.
- Extended MoE kernels to support multiple weight tensor configurations, improving flexibility for advanced quantization and distributed weight management scenarios.
Bug Fixes
- Fixed rank synchronization in disaggregated MoE initialization to use session-scoped barriers for improved compatibility.
Tests
- Added DWDP accuracy test for DeepSeek-V3-Lite model with GSM8K benchmark.

Description

In the context phase of LLM inference, workload imbalances and communication bottlenecks often lead to excessive synchronization overhead, limiting GPU utilization. To resolve this, we propose Distributed Weight Data Parallelism (DWDP), a strategy that leverages Data Parallelism combined with NVLink-based weight offloading to enable fully asynchronous execution across ranks.

Key properties of DWDP:

Context-phase acceleration: DWDP is designed specifically for the context (prefill) phase of disaggregated serving.
Hardware-aware: DWDP performance gains rely on the GB200 NVL72 architecture for high-bandwidth NVLink weight transfer.
Fully asynchronous execution: DWDP eliminates synchronization barriers across ranks, providing significant performance advantages under imbalanced workloads.
Fine-grained resource management: DWDP enables flexible expert-to-worker assignment, allowing more efficient GPU memory utilization.

A detailed technical report on DWDP internals and optimizations will be published separately. We welcome discussions and feedback.

Changes

Core DWDP runtime (tensorrt_llm/_torch/pyexecutor/dwdp.py — new file):

DwdpManager: Orchestrates IPC handle exchange across MPI ranks for zero-copy expert weight sharing
DwdpHandleCollector: Per-layer collector that gathers CUDA IPC handles for weight/scale/bias tensors
Expert weight prefetching with double-buffering via CUDA streams

MoE integration (configurable_moe.py, fused_moe_cute_dsl.py, interface.py):

Integrate DWDP into ConfigurableMoE with automatic detection of compatible backends (CuteDSL + NVFP4)
Add NvFp4WeightView dataclass for clean separation of DWDP vs non-DWDP weight access patterns
Add contiguous gather/scatter grouped GEMM kernels for DWDP expert subset computation

CuteDSL kernel extensions (cute_dsl_custom_ops.py, blockscaled_contiguous_*_fusion.py):

Extend blockscaled contiguous grouped GEMM kernels to support DWDP's gather-based expert selection
Unify single-B and multi-B kernel wrappers into a single entry point

Executor integration (py_executor.py, py_executor_creator.py, _util.py, llm_args.py):

Add DwdpConfig dataclass to LlmArgs for YAML-based DWDP configuration
Initialize DwdpManager during executor creation when DWDP is enabled
Wire DWDP expert prefetching into the per-step execution loop

Disaggregated serving scripts (examples/disaggregated/slurm/benchmark/):

Add start_worker_dwdp.sh for launching DWDP workers via mpirun
Extend submit.py with DWDP-specific configuration (dwdp_size, num_group, experts_per_worker, etc.)

Test Coverage

tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py: Unit tests for DWDP-specific CuteDSL MoE kernels (contiguous gather grouped GEMM with SwiGLU fusion, finalize fusion)
tests/scripts/cute_dsl_kernels/run_blockscaled_contiguous_gather_grouped_gemm_swiglu_fusion.py: Standalone kernel correctness tests for DWDP gather GEMM
tests/scripts/cute_dsl_kernels/run_blockscaled_contiguous_grouped_gemm_finalize_fusion.py: Standalone kernel correctness tests for DWDP finalize GEMM
tests/integration/defs/accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_dwdp_accuracy: End-to-end DWDP disaggregated serving accuracy test with DeepSeek-V3-Lite (NVFP4, 4 GPUs, GSM8K)

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.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.

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syuoni

This is a nice feature, great to see it's going to production.

Regarding the CuTeDSL MoE interface, overall I would suggest:

Use two different ops for single-b and multi-b cases.
Above the op level, we use two different code paths; this avoids confusion to users -- most users would use the single-b op only.
Below the op level, we unify them by multi-b implementation; this simplifies implementation and avoids code duplication.

Thanks!

…or MoE inference Core DWDP runtime (dwdp.py): - DwdpManager: IPC handle exchange across MPI ranks - DwdpHandleCollector: per-layer weight/scale/bias handle collection - Expert weight prefetching with double-buffering MoE integration (configurable_moe.py, fused_moe_cute_dsl.py, interface.py): - DWDP support in ConfigurableMoE with CuteDSL+NVFP4 backend - NvFp4WeightView for DWDP weight access patterns - Contiguous gather/scatter grouped GEMM kernels CuteDSL kernel extensions: - Blockscaled contiguous gather grouped GEMM with SwiGLU fusion - Blockscaled contiguous grouped GEMM finalize fusion Executor integration (py_executor.py, py_executor_creator.py, llm_args.py): - DwdpConfig dataclass for YAML-based configuration - DwdpManager initialization and per-step prefetching Disaggregated serving scripts: - start_worker_dwdp.sh for MPI-based worker launch - submit.py DWDP configuration support CI test: - DWDP accuracy test with DeepSeek-V3-Lite (NVFP4, 4 GPUs, GSM8K) Co-authored-by: wanqian-nv <221923321+wanqian-nv@users.noreply.github.com> Co-authored-by: zongfeijing <20381269+zongfeijing@users.noreply.github.com> Signed-off-by: tianyuz-nv <tianyuz@nvidia.com>

tianyuz-nv · 2026-03-19T08:17:29Z

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Signed-off-by: tianyuz-nv <tianyuz@nvidia.com>

…matting - Remove DwdpConfig.from_dict() to comply with Pydantic best practices test - Initialize GroupedGemmInputsHelper in test_nvfp4_gather_grouped_gemm_swiglu_blackwell - Apply pre-commit formatting (isort, yapf, ruff, autoflake, trailing whitespace) Signed-off-by: Tianyu Zhang <tianyuz@nvidia.com> Signed-off-by: tianyuz-nv <tianyuz@nvidia.com> Made-with: Cursor

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…nfigurableMoE load_weights PR NVIDIA#12136 (DWDP) added a load_weights override in CuteDslFusedMoE that dropped the allow_partial_loading parameter from the base class signature. ConfigurableMoE.load_weights also lacked this parameter. This causes TypeError when qwen2_moe_weight_mapper calls module.load_weights(weights=..., allow_partial_loading=...) on models using the CuteDSL or ConfigurableMoE backend (e.g., Qwen3 MoE). Signed-off-by: Xianjie <5410381+qiaoxj07@users.noreply.github.com>

…or MoE inference (NVIDIA#12136) Signed-off-by: tianyuz-nv <tianyuz@nvidia.com> Signed-off-by: Tianyu Zhang <tianyuz@nvidia.com> Signed-off-by: Kefeng-Duan <176893526+Kefeng-Duan@users.noreply.github.com> Co-authored-by: wanqian-nv <221923321+wanqian-nv@users.noreply.github.com> Co-authored-by: zongfeijing <20381269+zongfeijing@users.noreply.github.com> Co-authored-by: Kefeng-Duan <176893526+Kefeng-Duan@users.noreply.github.com>

… unused by VA path) Commit 3 of the DWDP IPC->VA refactor. Three files (custom op wrapper + two blackwell kernels) are restored verbatim to their pre-PR-NVIDIA#12136 state. These multi-B paths were introduced purely to support DWDP's IPC scheme, which passed N peer expert shards as separate B tensors into each kernel call. The VA pipeline swaps param.data to a single [num_experts, ...] tensor via cuMemMap, so the standard single-B kernel path handles every case — the multi-B parameters, MAX_B_TENSORS branches, and tuple-ified b/sfb/alpha signatures become dead code. Files reverted to the commit before e92ee4f (PR NVIDIA#12136): * _torch/custom_ops/cute_dsl_custom_ops.py - Removes *_multi_b custom op registrations - Restores GatherGroupedGemmInputsHelper to single-tensor layout * _torch/cute_dsl_kernels/blackwell/blockscaled_contiguous_gather_grouped_gemm_swiglu_fusion.py - Removes b_tensor_l_sizes param, MAX_B_TENSORS, num_b_tensors const_expr branches for 1/2/3/4 B tensors, _make_tma_b helper, kernel-side gB_nkl_0..3 / gSFB_nkl_0..3 expansions, tuple'd signatures * _torch/cute_dsl_kernels/blackwell/blockscaled_contiguous_grouped_gemm_finalize_fusion.py - Same pattern Verified no commits touched these files between PR NVIDIA#12136 and HEAD, so the revert is surgical and does not risk clobbering unrelated work: $ git log --oneline e92ee4f..HEAD -- <file> # empty for all 3 Net change: +371 / -1519 = -1148 lines in the three files. Smoke tested: 78 pytest passes (DWDP units + full api_stability), plus runtime import checks confirm both kernel modules and the custom ops module load cleanly after the revert. Co-Authored-By: dongxuy04 <dongxuy@nvidia.com> Signed-off-by: tianyuz-nv <tianyuz@nvidia.com>

`num_groups` has been a dead schema field in DwdpConfig from PR NVIDIA#12136 (the original IPC implementation) through commit 1fbc0d4: read into DwdpManager but never consumed by the runtime. Mis-configured YAMLs where the user's declared topology disagrees with the launch were left to fail mysteriously deeper in MPI sub-communicator creation, or to silently accept a launch that didn't match the schema. Convert the field into runtime validation in DwdpManager.__init__: 1. num_groups must be positive. 2. This rank's computed group_id (`rank // dwdp_size`) must be less than num_groups, so the launch hasn't started more CTX workers than the declared topology can hold. 3. `num_groups * dwdp_size <= MPI world size`, so the world is large enough to fit all declared groups. The three checks together catch over-subscription, world-size under-allocation, and obviously bogus values, while remaining local (no extra inter-rank communication required since DwdpConfig is identical on every rank). Verification: - Unit tests: 61 passed + 4 subtests (4 new num_groups cases in tests/unittest/_torch/executor/test_dwdp_manager.py) - Accuracy (DSv3-Lite + dwdp=2 + GSM8K): PASS, above the 61.5% threshold (3min 52s) - Perf DEP baseline: 12.95 req/s - Perf DWDP=4: 14.26 req/s (+10.1% over DEP) - Perf DWDP=8 cross-tray: 27.21 req/s (1.91x DWDP=4 scaling) Signed-off-by: tianyuz-nv <tianyuz@nvidia.com>

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tianyuz-nv changed the title ~~Dwdp productization~~ [None][feat] Add DWDP (Disaggregated Weight Data Parallelism) support for MoE inference Mar 17, 2026

tianyuz-nv changed the title ~~[None][feat] Add DWDP (Disaggregated Weight Data Parallelism) support for MoE inference~~ [None][feat] Add DWDP (Distributed Weight Data Parallelism) support for MoE inference Mar 17, 2026

tianyuz-nv force-pushed the dwdp_productization branch 2 times, most recently from 1d3e509 to 3532338 Compare March 19, 2026 05:55

tianyuz-nv force-pushed the dwdp_productization branch from 3532338 to bbe48fa Compare March 19, 2026 08:09

tianyuz-nv added 3 commits March 22, 2026 22:47

Merge upstream/main into dwdp_productization

7e4a855

Signed-off-by: tianyuz-nv <tianyuz@nvidia.com>

Register DWDP accuracy test in CI test lists

f76d026

Signed-off-by: tianyuz-nv <tianyuz@nvidia.com>

tianyuz-nv marked this pull request as ready for review March 24, 2026 02:39

tianyuz-nv requested review from a team as code owners March 24, 2026 02:39

Merge remote-tracking branch 'upstream/main' into dwdp_productization

be12482

Kefeng-Duan enabled auto-merge (squash) April 2, 2026 03:29

Kefeng-Duan merged commit e92ee4f into NVIDIA:main Apr 2, 2026
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tianyuz-nv mentioned this pull request Apr 2, 2026

[None][fix] Fix CuteDslFusedMoE.load_weights signature to accept allow_partial_loading #12690

Closed

hlu1 mentioned this pull request Apr 4, 2026

[Feature] Distributed Weight Data Parallelism (DWDP) for Sparse MoE Models sgl-project/sglang#22084

Open

qiaoxj07 mentioned this pull request Apr 4, 2026

[None][fix] Add missing allow_partial_loading param to CuteDSL and ConfigurableMoE load_weights #12761

Merged

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hlu1 mentioned this pull request Apr 11, 2026

[CuteDSL MoE] Update the CuteDSL MoE kernels support DWDP flashinfer-ai/flashinfer#3036

Open

yhyang201 mentioned this pull request Apr 13, 2026

[WIP] feat: support multi-B weight tensors (DWDP) in CuTe DSL NVFP4 MoE flashinfer-ai/flashinfer#3041

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