[None][chore] Fix kernel launch param and add TRTLLM MoE backend test #7524
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| max_batch_size): | ||
| if get_sm_version() == 100: | ||
| moe_config = MoeConfig(backend="DEEPGEMM", max_num_tokens=16384) | ||
| moe_config = MoeConfig(backend="TRTLLM", max_num_tokens=16384) |
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@pengbowang-nv , I think we have to keep "DEEPGEMM" backend test, and add TRTLLM, not replacement.
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Signed-off-by: Pengbo Wang <221450789+pengbowang-nv@users.noreply.github.com>
Signed-off-by: Pengbo Wang <221450789+pengbowang-nv@users.noreply.github.com>
Signed-off-by: Pengbo Wang <221450789+pengbowang-nv@users.noreply.github.com>
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📝 WalkthroughWalkthroughCaps CUDA kernel launch grid dimensions at 8192 in DevKernel.cu; updates Jenkins GB200 multi-node test splits from 4 to 5 and adds a PyTorch-5 entry; extends PyTorch integration tests to parameterize/select MoE backends; adds new fp8_blockscale throughput_mtp_trtllm test entries to DGX B200 and GB200 multi-node test lists. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant T as Test Runner
participant P as Parametrize (moe_backend)
participant D as Device Query (SM)
participant C as Config (MoeConfig)
participant S as Skipper
T->>P: Get moe_backend param
P->>D: Query SM version
alt _DEFAULT
D-->>P: SM value
P->>C: backend = DEEPGEMM if SM==100 else default path
C-->>T: MoeConfig(backend=resolved)
T->>T: Run test with config
else Explicit backend (e.g., TRTLLM)
D-->>P: SM value
alt Supported (e.g., SM==100)
P->>C: backend = requested
C-->>T: MoeConfig(backend=requested)
T->>T: Run test with config
else Unsupported
P->>S: Mark test as skipped
S-->>T: Skip
end
end
sequenceDiagram
autonumber
participant L as Launcher
participant K as Kernels
participant M as Metrics
L->>L: tokens = data.numTokens
L->>L: capped = min(8192, tokens)
L->>K: Launch activation kernels with grid.z=capped
L->>K: Launch permute/finalize with numBlocksY=capped
alt Finalize path selection
L->>L: Evaluate threshold using capped
L->>K: finalizeKernel or finalizeKernelVecLoad
end
K-->>M: Execution metrics (unchanged compute)
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Pre-merge checks (1 passed, 2 warnings)❌ Failed checks (2 warnings)
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⚠️ Outside diff range comments (1)
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml (1)
3-15: Correct system_gpu_count for l0_gb200_multi_nodes.yml
In tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml,system_gpu_countis sourced from each node’s local GPU count (see get_sysinfo.py:system_gpu_count = num_gpus), so it should be 4, not 8.- system_gpu_count: - gte: 8 - lte: 8 + system_gpu_count: + gte: 4 + lte: 4
🧹 Nitpick comments (3)
cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/DevKernel.cu (2)
189-197: Use a named constant and ensure std::min is available
- Define a single kMAX_GRID_DIM_YZ and use std::min(...) to avoid type surprises.
- Add include explicitly to prevent relying on transitive headers.
Apply diff within these ranges:
-const dim3 grid(data.innerDim / 128, data.topK, std::min(8192, data.numTokens)); +const dim3 grid(data.innerDim / 128, data.topK, std::min<int>(kMAX_GRID_DIM_YZ, data.numTokens));-int const numBlocksY = std::min(8192, data.numTokens); +int const numBlocksY = std::min<int>(kMAX_GRID_DIM_YZ, data.numTokens);Additional changes outside the selected ranges (supporting code):
// At file top near other includes #include <algorithm> // Near other module-level constants constexpr int kMAX_GRID_DIM_YZ = 8192;Also applies to: 374-375, 460-461
701-719: Vectorized finalize selection may be skewed by capped YThe decision uses numBlocksY capped at 8192, which can under-estimate waves for very large token counts and pick the non-vectorized path. If the intent is to decide by total work, compute the threshold with data.numTokens (uncapped), but keep the launch cap.
- int const numBlocksY = std::min(8192, data.numTokens); + int const numBlocksY = std::min<int>(kMAX_GRID_DIM_YZ, data.numTokens); if (numBlocksX * numBlocksY < 1184) + // Consider: if (static_cast<long long>(numBlocksX) * static_cast<long long>(data.numTokens) < 1184) {If you want, I can draft a quick micro-benchmark harness to compare both choices on SM100/SM120.
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml (1)
16-21: Confirm 180-minute TIMEOUT is intentional and necessary (minutes, not seconds).
If the P95 runtime for these DeepSeek R1 throughput tests consistently falls well below 120 minutes, consider lowering the TIMEOUT to 120 minutes to reduce CI queue pressure.
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📒 Files selected for processing (5)
cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/DevKernel.cu(3 hunks)jenkins/L0_Test.groovy(1 hunks)tests/integration/defs/accuracy/test_llm_api_pytorch.py(1 hunks)tests/integration/test_lists/test-db/l0_dgx_b200.yml(1 hunks)tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml(1 hunks)
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cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/DevKernel.cu
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🧠 Learnings (8)
📓 Common learnings
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.
📚 Learning: 2025-08-13T11:07:11.772Z
Learnt from: Funatiq
PR: NVIDIA/TensorRT-LLM#6754
File: tests/integration/test_lists/test-db/l0_a30.yml:41-47
Timestamp: 2025-08-13T11:07:11.772Z
Learning: In TensorRT-LLM test configuration files like tests/integration/test_lists/test-db/l0_a30.yml, TIMEOUT values are specified in minutes, not seconds.
Applied to files:
tests/integration/test_lists/test-db/l0_dgx_b200.ymltests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml
📚 Learning: 2025-08-26T09:49:04.956Z
Learnt from: pengbowang-nv
PR: NVIDIA/TensorRT-LLM#7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").
Applied to files:
tests/integration/test_lists/test-db/l0_dgx_b200.yml
📚 Learning: 2025-09-09T09:40:45.631Z
Learnt from: fredricz-20070104
PR: NVIDIA/TensorRT-LLM#7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.631Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.
Applied to files:
tests/integration/test_lists/test-db/l0_dgx_b200.ymltests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml
📚 Learning: 2025-08-14T21:04:50.248Z
Learnt from: thorjohnsen
PR: NVIDIA/TensorRT-LLM#6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.
Applied to files:
cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/DevKernel.cu
📚 Learning: 2025-08-09T20:57:04.084Z
Learnt from: sklevtsov-nvidia
PR: NVIDIA/TensorRT-LLM#3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu:118-127
Timestamp: 2025-08-09T20:57:04.084Z
Learning: In the CUTLASS MoE finalize fusion implementation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu), when setting `fused_finalize_epilogue.stride_final_output` with shape `(hidden_size, num_output_tokens, 1)`, the `num_rows_in_final_output` should be set to `num_output_tokens` (not `hidden_size`) because of a swap+transpose operation that maps rows of the output tensor to `hidden_size` and columns to `num_output_tokens`.
Applied to files:
cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/DevKernel.cu
📚 Learning: 2025-08-08T22:03:40.707Z
Learnt from: sklevtsov-nvidia
PR: NVIDIA/TensorRT-LLM#3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1198-1209
Timestamp: 2025-08-08T22:03:40.707Z
Learning: In the CUTLASS MoE kernels (cpp/tensorrt_llm/cutlass_extensions), when `layout_info.fusion` is set to `TmaWarpSpecializedGroupedGemmInput::EpilogueFusion::FINALIZE`, the `router_scales` parameter must be non-null by design. The fused finalize kernel epilogue does not perform nullptr checks and requires valid router scales to function correctly. This is an implicit contract that callers must satisfy when enabling the FINALIZE fusion mode.
Applied to files:
cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/DevKernel.cu
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
🧬 Code graph analysis (1)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (2)
tests/integration/defs/conftest.py (1)
get_sm_version(1857-1860)tensorrt_llm/llmapi/llm_args.py (2)
MoeConfig(168-202)KvCacheConfig(967-1098)
🔇 Additional comments (4)
tests/integration/test_lists/test-db/l0_dgx_b200.yml (1)
74-74: Approve TRTLLM throughput variant entry
- Parametrization id “throughput_mtp_trtllm” is present in TestDeepSeekR1::test_fp8_blockscale ids list.
- TIMEOUT (180) is consistent with prior entries.
jenkins/L0_Test.groovy (1)
2096-2101: Ensure 5-way splitting yields non-empty groups
Config entries for splits 1–5 are defined. Please confirm thatl0_gb200_multi_nodesreturns at least five test cases so no split ends up empty.tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)
1921-1928: Param set adds TRTLLM throughput variantThe ids align with the DB entry you added (“throughput_mtp_trtllm”). Looks good.
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml (1)
20-20: Approve TRTLLM MoE throughput-MTP test entry. Parameterized IDthroughput_mtp_trtllmis defined inTestDeepSeekR1::test_fp8_blockscalewithintests/integration/defs/accuracy/test_llm_api_pytorch.py, confirming the entry underpre_mergeis correct.
| if get_sm_version() == 100: | ||
| moe_config = MoeConfig(backend="DEEPGEMM", max_num_tokens=16384) | ||
| moe_backend = "DEEPGEMM" if moe_backend == "_DEFAULT" else moe_backend | ||
| moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384) | ||
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6) | ||
| else: | ||
| if moe_backend != "_DEFAULT": | ||
| pytest.skip("Not supported MoE backend!") | ||
| moe_config = MoeConfig() | ||
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.9) |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
TRTLLM backend is skipped on SM120 but is being scheduled on GB200
Current gating allows TRTLLM only for SM==100. GB200 (SM120) multi-node lists include the TRTLLM variant; this will be skipped here. If TRTLLM is intended on SM120, widen the check to >=100.
- if get_sm_version() == 100:
+ if get_sm_version() >= 100:
moe_backend = "DEEPGEMM" if moe_backend == "_DEFAULT" else moe_backend
moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384)
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6)
else:
if moe_backend != "_DEFAULT":
- pytest.skip("Not supported MoE backend!")
+ pytest.skip("MoE backend not supported on pre-Blackwell GPUs; using default only.")
moe_config = MoeConfig()
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.9)Confirm intent: should TRTLLM run on GB200?
🏁 Script executed:
#!/bin/bash
rg -n "l0_gb200_multi_nodes" tests/integration/test_lists/test-db -n
rg -n "throughput_mtp_trtllm" tests/integration/test_lists/test-dbLength of output: 428
Widen SM version check for TRTLLM to include SM120
Test lists for GB200 include throughput_mtp_trtllm (l0_gb200_multi_nodes.yml:20, l0_dgx_b200.yml:74), but code currently only allows get_sm_version() == 100. If TRTLLM support on SM120 is intended, apply the diff below; otherwise remove this variant from GB200 lists.
- if get_sm_version() == 100:
+ if get_sm_version() >= 100:
moe_backend = "DEEPGEMM" if moe_backend == "_DEFAULT" else moe_backend
moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384)
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6)
else:
if moe_backend != "_DEFAULT":
- pytest.skip("Not supported MoE backend!")
+ pytest.skip("MoE backend not supported on pre-Blackwell GPUs; using default only.")
moe_config = MoeConfig()
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.9)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if get_sm_version() == 100: | |
| moe_config = MoeConfig(backend="DEEPGEMM", max_num_tokens=16384) | |
| moe_backend = "DEEPGEMM" if moe_backend == "_DEFAULT" else moe_backend | |
| moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384) | |
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6) | |
| else: | |
| if moe_backend != "_DEFAULT": | |
| pytest.skip("Not supported MoE backend!") | |
| moe_config = MoeConfig() | |
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.9) | |
| if get_sm_version() >= 100: | |
| moe_backend = "DEEPGEMM" if moe_backend == "_DEFAULT" else moe_backend | |
| moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384) | |
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6) | |
| else: | |
| if moe_backend != "_DEFAULT": | |
| pytest.skip("MoE backend not supported on pre-Blackwell GPUs; using default only.") | |
| moe_config = MoeConfig() | |
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.9) |
|
The added tests were passing on B200, skipping merge. The risk is minimal, and only this new tests are using the code path. |
|
PR_Github #18228 [ run ] completed with state |
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Run
/bot [-h|--help]to print this help message.See details below for each supported subcommand.
run [--reuse-test (optional)pipeline-id --disable-fail-fast --skip-test --stage-list "A10-PyTorch-1, xxx" --gpu-type "A30, H100_PCIe" --test-backend "pytorch, cpp" --add-multi-gpu-test --only-multi-gpu-test --disable-multi-gpu-test --post-merge --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx" --detailed-log --debug(experimental)]Launch build/test pipelines. All previously running jobs will be killed.
--reuse-test (optional)pipeline-id(OPTIONAL) : Allow the new pipeline to reuse build artifacts and skip successful test stages from a specified pipeline or the last pipeline if no pipeline-id is indicated. If the Git commit ID has changed, this option will be always ignored. The DEFAULT behavior of the bot is to reuse build artifacts and successful test results from the last pipeline.--disable-reuse-test(OPTIONAL) : Explicitly prevent the pipeline from reusing build artifacts and skipping successful test stages from a previous pipeline. Ensure that all builds and tests are run regardless of previous successes.--disable-fail-fast(OPTIONAL) : Disable fail fast on build/tests/infra failures.--skip-test(OPTIONAL) : Skip all test stages, but still run build stages, package stages and sanity check stages. Note: Does NOT update GitHub check status.--stage-list "A10-PyTorch-1, xxx"(OPTIONAL) : Only run the specified test stages. Examples: "A10-PyTorch-1, xxx". Note: Does NOT update GitHub check status.--gpu-type "A30, H100_PCIe"(OPTIONAL) : Only run the test stages on the specified GPU types. Examples: "A30, H100_PCIe". Note: Does NOT update GitHub check status.--test-backend "pytorch, cpp"(OPTIONAL) : Skip test stages which don't match the specified backends. Only support [pytorch, cpp, tensorrt, triton]. Examples: "pytorch, cpp" (does not run test stages with tensorrt or triton backend). Note: Does NOT update GitHub pipeline status.--only-multi-gpu-test(OPTIONAL) : Only run the multi-GPU tests. Note: Does NOT update GitHub check status.--disable-multi-gpu-test(OPTIONAL) : Disable the multi-GPU tests. Note: Does NOT update GitHub check status.--add-multi-gpu-test(OPTIONAL) : Force run the multi-GPU tests in addition to running L0 pre-merge pipeline.--post-merge(OPTIONAL) : Run the L0 post-merge pipeline instead of the ordinary L0 pre-merge pipeline.--extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx"(OPTIONAL) : Run the ordinary L0 pre-merge pipeline and specified test stages. Examples: --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx".--detailed-log(OPTIONAL) : Enable flushing out all logs to the Jenkins console. This will significantly increase the log volume and may slow down the job.--debug(OPTIONAL) : Experimental feature. Enable access to the CI container for debugging purpose. Note: Specify exactly one stage in thestage-listparameter to access the appropriate container environment. Note: Does NOT update GitHub check status.For guidance on mapping tests to stage names, see
docs/source/reference/ci-overview.mdand the
scripts/test_to_stage_mapping.pyhelper.kill
killKill all running builds associated with pull request.
skip
skip --comment COMMENTSkip testing for latest commit on pull request.
--comment "Reason for skipping build/test"is required. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.reuse-pipeline
reuse-pipelineReuse a previous pipeline to validate current commit. This action will also kill all currently running builds associated with the pull request. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.