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WalkthroughThis change adds native FP8 adapter support for routed-expert MoE LoRA. It propagates adapter cache dtype through loading and execution, converts FP8 inputs for grouped GEMM, and supports a separate GEMM output dtype. ChangesFP8 MoE LoRA
Priority: Estimated code review effort: 4 (Complex) | ~60 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant FusedMoeRunner
participant runMoe
participant runMoeLoraGroupedGemmModule
participant FP8ConversionKernel
participant GroupedGEMM
FusedMoeRunner->>runMoe: Pass LoRA cache dtype
runMoe->>runMoeLoraGroupedGemmModule: Pass input and output dtypes
runMoeLoraGroupedGemmModule->>FP8ConversionKernel: Convert FP8 inputs and zero padded rows
FP8ConversionKernel->>GroupedGEMM: Provide converted inputs
runMoeLoraGroupedGemmModule->>GroupedGEMM: Dispatch with compute output dtype
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
Docstring coverage is 42.70% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 89 functions across 19 files. (1 skipped:… | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Title check The title clearly identifies the feature, uses a valid ticket and type format, and matches the main changes. Description check The description includes the required sections, explains the feature and implementation, documents test coverage, reports validation results, and notes a known performance follow-up. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request.
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Docstring coverage is 42.70% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 89 functions across 19 files. (1 skipped: 1 unsupported.)
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Write docstrings for the functions missing them to satisfy the coverage threshold.
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@tests/integration/defs/llmapi/test_llm_api_pytorch_moe_lora.py:
- Around line 461-465: Skip test_qwen_moe_native_fp8_lora_matches_bf16_reference
when CUDA is unavailable or
supports_native_fp8_lora(torch.cuda.get_device_capability()) is false. Import
supports_native_fp8_lora from the existing LoRA manager module and place the
skipif marker alongside the test parametrization, preserving execution only on
supported native FP8 LoRA devices.
Review comments at @tests/unittest/_torch/peft/test_moe_lora_op.py:
- Around line 1253-1255: Gate test_moe_fp8_adapter_weights_change_output with
the native FP8 capability check used by the grouped-GEMM tests. Import
supports_native_fp8_lora, define the corresponding requires_native_fp8 skip
marker using the CUDA device capability, and apply it alongside
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cpp/tensorrt_llm/kernels/cuda_graph_grouped_gemm.cucpp/tensorrt_llm/kernels/cuda_graph_grouped_gemm.hcpp/tensorrt_llm/kernels/moe/cutlass/include/moe_kernels.hcpp/tensorrt_llm/kernels/moe/cutlass/include/moe_lora_grouped_gemm.hcpp/tensorrt_llm/kernels/moe/cutlass/include/moe_lora_problem_builder.hcpp/tensorrt_llm/kernels/moe/cutlass/moe_kernels.cucpp/tensorrt_llm/kernels/moe/cutlass/moe_lora_problem_builder.cucpp/tensorrt_llm/thop/moe/cutlass/moeOp.cppcpp/tests/unit_tests/kernels/moe/moeLoraProblemBuilderTest.cudocs/source/features/lora.mdtensorrt_llm/_torch/custom_ops/torch_custom_ops.pytensorrt_llm/_torch/moe/fused_moe/fused_moe_cutlass.pytensorrt_llm/_torch/peft/lora/loaders.pytensorrt_llm/_torch/peft/lora/manager.pytests/integration/defs/llmapi/test_llm_api_pytorch_moe_lora.pytests/integration/test_lists/test-db/l0_h100.ymltests/unittest/_torch/peft/test_moe_lora_cuda_graph_params.pytests/unittest/_torch/peft/test_moe_lora_extract.pytests/unittest/_torch/peft/test_moe_lora_grouped_gemm.pytests/unittest/_torch/peft/test_moe_lora_op.pytests/unittest/others/test_lora_manager.py
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- 🪄 Fix comments: Fix CodeRabbit comments on this PR
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Review comments at @tests/unittest/_torch/peft/test_moe_lora_op.py:
- Around line 1304-1311: Strengthen the FP8 validation in the native MoE LoRA
test by upcasting the same FP8-rounded adapter tensors to BF16, building
equivalent LoRA arguments, and running the BF16 reference through
_call_fused_moe. Compare its output with out_lora using
torch.testing.assert_close with suitable FP8 tolerances, while preserving the
existing finite-output and observable-effect assertions.
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tests/integration/defs/llmapi/test_llm_api_pytorch_moe_lora.pytests/unittest/_torch/peft/test_moe_lora_op.py
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Load native FP8 routed-expert adapters and carry their cache dtype through MoE grouped GEMMs. Reuse existing LoRA workspace with capacity checks and store final deltas directly in the activation compute dtype. Validate adapter dtypes, alignment and rank bounds; preserve scratch capacity across dtype changes. Add FP32-reference, mixed-dtype, stride, and CUDA graph regression coverage plus a small synthetic integration fixture. Document FP8 rounding and adapter storage savings. Pre-commit and CPU fixture checks passed. CUDA build and GPU validation remain pending because Computelab reservations were unavailable. Signed-off-by: Aurelien Chartier <2567591+achartier@users.noreply.github.com>
Run the BF16 path with the same E4M3-rounded adapter values for both LoRA schemas and bound the relative delta error. Keep the finite-output and observable-effect checks. Signed-off-by: Aurelien Chartier <2567591+achartier@users.noreply.github.com>
Remove the 180-minute overrides for the tiny synthetic eager and CUDA graph cases. Use the standard 3600-second CI timeout until measured runtime warrants an override. Signed-off-by: Aurelien Chartier <2567591+achartier@users.noreply.github.com>
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Description
Add native FP8 E4M3 LoRA adapter support for routed-expert MoE layers in the PyTorch backend.
The change:
The conversion workspace is currently reserved for every MoE LoRA run, including BF16 adapters. The measured overhead is acceptable for this initial implementation but remains a performance follow-up.
Test Coverage
tests/integration/defs/llmapi/test_llm_api_pytorch_moe_lora.py::test_qwen_moe_native_fp8_lora_matches_bf16_reference[eager]tests/integration/defs/llmapi/test_llm_api_pytorch_moe_lora.py::test_qwen_moe_native_fp8_lora_matches_bf16_reference[cudagraph]l0_h100.ymland map to the H100 PyTorch post-merge stages.PR Checklist
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/bot help.Dev Engineer Review
Adds native FP8 E4M3 LoRA support for routed-expert MoE layers. The change preserves FP8 adapter weights, validates dtype and rank constraints, converts adapters in grouped-GEMM execution, propagates per-module ranks, and updates workspace handling.
Grouped-GEMM APIs now accept separate input and output dtypes. Verify API consistency, FP8 build and hardware guards, buffer sizing, and CUDA graph replay. Conversion workspace is reserved for MoE LoRA runs, including BF16 adapters; this adds a performance consideration. Review severity counts are unavailable because no current review findings were supplied.
QA Engineer Review
Tests cover FP8 cache preservation and dtype propagation, rank validation, mixed-dtype rejection, eager execution, CUDA graph replay, empty slots, scratch reuse, output changes, and FP8-versus-BF16 accuracy. The eager and CUDA graph integration cases are listed in
tests/integration/test_lists/test-db/l0_h100.yml.The supplied report says both H100 integration cases passed, and reports a clean Release build, passing pre-commit checks, and successful Python compilation checks. PR comments also report four CI runs with
FAILUREstatus and oneUNSTABLErun. The comments do not include failure details. Coverage verdict:sufficient.Per-File QA Perspective
cpp/tensorrt_llm/kernels/cuda_graph_grouped_gemm.cu: Verify FP8, FP16, and BF16 output dispatch, default output dtype behavior, and unsupported-type errors.cpp/tensorrt_llm/kernels/cuda_graph_grouped_gemm.h: Verify source compatibility for callers that omit the optional output dtype.cpp/tensorrt_llm/kernels/moe/cutlass/include/moe_kernels.h: Verify FC1 and FC2 result-buffer sizes match their allocated workspaces.cpp/tensorrt_llm/kernels/moe/cutlass/include/moe_lora_grouped_gemm.h: Verify the grouped-GEMM function pointer and per-invocation dtype stay consistent.cpp/tensorrt_llm/kernels/moe/cutlass/include/moe_lora_problem_builder.h: Verify callers provide the output byte width.cpp/tensorrt_llm/kernels/moe/cutlass/moe_kernels.cu: Verify FP8 conversion, padding, empty-buffer handling, dtype checks, and capacity checks.cpp/tensorrt_llm/kernels/moe/cutlass/moe_lora_problem_builder.cu: Verify output-row strides useoutput_dtype_byteswhile input and workspace strides usedtype_bytes.cpp/tensorrt_llm/thop/moe/cutlass/moeOp.cpp: Verify dtype validation, FP8 rank alignment and hardware guards, scratch reuse, and rank propagation.tensorrt_llm/_torch/custom_ops/torch_custom_ops.py: Verify bothfused_moeimplementations accept and forwardlora_dtype.tensorrt_llm/_torch/moe/fused_moe/fused_moe_cutlass.py: Verify eager and CUDA graph paths preserve adapter dtype.tensorrt_llm/_torch/peft/lora/loaders.py: Verify dtype detection includes expert LoRA weights and rejects mixed dtypes.tensorrt_llm/_torch/peft/lora/manager.py: Verify native FP8 cache preservation and rejection of unsupported or mixed-dtype adapters.docs/source/features/lora.md: Verify documented dtype, rank, dimension, quantization, and hardware constraints match implementation behavior.tests/integration/defs/llmapi/test_llm_api_pytorch_moe_lora.py: Covers FP8-versus-BF16 comparison, cache dtype, rank metadata, eager execution, and CUDA graph replay. Both integration selectors are listed inl0_h100.yml.tests/integration/test_lists/test-db/l0_h100.yml: Adds eager and CUDA graph H100 selectors for the native FP8 LoRA integration test.tests/unittest/_torch/peft/test_moe_lora_cuda_graph_params.py: Covers FP8 dtype preservation in CUDA graph arguments. This unit-test file is not listed in the integration CI database or manual-QA lists.tests/unittest/_torch/peft/test_moe_lora_extract.py: Covers FP8 metadata extraction and the no-active-MoE case. This unit-test file is not listed in the integration CI database or manual-QA lists.tests/unittest/_torch/peft/test_moe_lora_grouped_gemm.py: Covers FP8 replay, accuracy, clipping, scratch reuse, and rank rejection. This unit-test file is not listed in the integration CI database or manual-QA lists.tests/unittest/_torch/peft/test_moe_lora_op.py: Covers FP8 output changes for per-request and slot-indexed schemas. This unit-test file is not listed in the integration CI database or manual-QA lists.tests/unittest/others/test_lora_manager.py: Covers dtype detection, cache preservation, mixed-dtype rejection, rank validation, and cache non-mutation. This unit-test file is not listed in the integration CI database or manual-QA lists.