[TRTLLM-9872][fix] clear the failed test at CI when enalbe_configurab… #10067
Conversation
…le_moe Signed-off-by: xxi <xxi@nvidia.com>
|
/bot run --disable-fail-fast |
📝 WalkthroughWalkthroughThe changes introduce a QuantConfig-based override parameter to ConfigurableMoE for backend selection, update workspace sizing logic to account for distributed padding via ep_size computation, simplify multi-stream workspace handling, add guards to quantization method selection, and refactor test infrastructure to use model config-driven MoE creation. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes
Pre-merge checks and finishing touches❌ Failed checks (3 warnings)
✨ Finishing touches
🧪 Generate unit tests (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 0
Caution
Some comments are outside the diff and can’t be posted inline due to platform limitations.
⚠️ Outside diff range comments (1)
tests/unittest/_torch/modules/test_awq_quantization.py (1)
1-14: Missing NVIDIA copyright header.Per coding guidelines, all TensorRT-LLM source files should contain an NVIDIA copyright header with the current year. This test file appears to be missing the required header.
Add the copyright header at the top of the file:
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from unittest.mock import patchAs per coding guidelines, all TensorRT-LLM source code files should contain an NVIDIA copyright header.
🧹 Nitpick comments (1)
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (1)
110-110: Remove unnecessary string reference for type annotation.
QuantConfigis imported at line 40, so the forward reference string is unnecessary.- override_quant_config: Optional["QuantConfig"] = None, + override_quant_config: Optional[QuantConfig] = None,
📜 Review details
Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (4)
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py(5 hunks)tensorrt_llm/_torch/modules/fused_moe/create_moe.py(2 hunks)tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py(1 hunks)tests/unittest/_torch/modules/test_awq_quantization.py(2 hunks)
🧰 Additional context used
📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.py: Code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces. Do not use tabs
Always maintain the namespace when importing, even if only one class or function from a module is used (e.g., usefrom package.subpackage import foo; foo.SomeClass()instead offrom package.subpackage.foo import SomeClass)
Python filenames should use snake_case (e.g.,some_file.py)
Python classes should use PascalCase (e.g.,class SomeClass)
Python functions and methods should use snake_case (e.g.,def my_awesome_function():)
Python local variables should use snake_case. Prefix 'k' for variable names that start with a number (e.g.,k_99th_percentile = ...)
Python global variables should use upper snake_case and prefix 'G' (e.g.,G_MY_GLOBAL = ...)
Python constants should use upper snake_case (e.g.,MY_CONSTANT = ...)
Avoid shadowing variables declared in an outer scope in Python code
Initialize all externally visible members of a Python class in the constructor
For Python interfaces that may be used outside a file, prefer docstrings over comments
Python comments should be reserved for code within a function, or interfaces that are local to a file
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx
Python attributes and variables can be documented inline with attribute docstrings that will be rendered under the class docstring
Avoid using reflection in Python when functionality can be easily achieved without reflection
When using try-except blocks in Python, limit the except to the smallest set of errors possible
When using try-except blocks in Python to handle multiple possible variable types (duck-typing), keep the body of the try as small as possible, using the else block to implement the logic
Files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.pytests/unittest/_torch/modules/test_awq_quantization.pytensorrt_llm/_torch/modules/fused_moe/configurable_moe.pytensorrt_llm/_torch/modules/fused_moe/create_moe.py
**/*.{h,hpp,cpp,cc,cxx,cu,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
All TensorRT-LLM source code files should contain an NVIDIA copyright header with the current year, including .cpp, .h, .cu, .py, and other source files
Files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.pytests/unittest/_torch/modules/test_awq_quantization.pytensorrt_llm/_torch/modules/fused_moe/configurable_moe.pytensorrt_llm/_torch/modules/fused_moe/create_moe.py
🧠 Learnings (10)
📓 Common learnings
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.
📚 Learning: 2025-08-14T23:23:27.449Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 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.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.pytensorrt_llm/_torch/modules/fused_moe/configurable_moe.pytensorrt_llm/_torch/modules/fused_moe/create_moe.py
📚 Learning: 2025-09-19T21:28:13.751Z
Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.pytensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
📚 Learning: 2025-08-21T02:39:12.009Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.pytensorrt_llm/_torch/modules/fused_moe/create_moe.py
📚 Learning: 2025-08-29T14:07:45.863Z
Learnt from: EmmaQiaoCh
Repo: NVIDIA/TensorRT-LLM PR: 7370
File: tests/unittest/trt/model_api/test_model_quantization.py:24-27
Timestamp: 2025-08-29T14:07:45.863Z
Learning: In TensorRT-LLM's CI infrastructure, pytest skip markers (pytest.mark.skip) are properly honored even when test files have __main__ blocks that call test functions directly. The testing system correctly skips tests without requiring modifications to the __main__ block execution pattern.
Applied to files:
tests/unittest/_torch/modules/test_awq_quantization.py
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM's bench configuration, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which is a Dict[str, Any] that can contain default values including `cuda_graph_config`, making the fallback `llm_args["cuda_graph_config"]` safe to use.
Applied to files:
tests/unittest/_torch/modules/test_awq_quantization.py
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.
Applied to files:
tests/unittest/_torch/modules/test_awq_quantization.py
📚 Learning: 2025-09-23T14:58:05.372Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/config.cu:42-49
Timestamp: 2025-09-23T14:58:05.372Z
Learning: In TensorRT-LLM NCCL device kernels (cpp/tensorrt_llm/kernels/nccl_device/), the token partitioning intentionally uses ceil-like distribution (same token_per_rank for all ranks) to ensure all ranks launch the same number of blocks. This is required for optimal NCCL device API barrier performance, even though it may launch extra blocks for non-existent tokens on later ranks. Runtime bounds checking in the kernel (blockID validation) handles the overshoot cases.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
📚 Learning: 2025-08-19T12:45:11.997Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/create_moe.py
📚 Learning: 2025-11-14T11:22:03.729Z
Learnt from: nzmora-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 9163
File: tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py:107-113
Timestamp: 2025-11-14T11:22:03.729Z
Learning: In TensorRT-LLM AutoDeploy custom ops, when adding hardware capability checks to select between kernel implementations (e.g., cuBLAS vs. CUDA kernel), use descriptive variable names that identify the specific GPU architectures or families being targeted (e.g., `is_blackwell_geforce_or_ada`) rather than generic names like `enable_cuda_core`. This makes it clear that the code is selecting an implementation path based on hardware capabilities, not enabling/disabling hardware features.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/create_moe.py
🧬 Code graph analysis (3)
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py (3)
tensorrt_llm/_torch/modules/fused_moe/interface.py (1)
has_any_quant(668-671)tensorrt_llm/_torch/modules/linear.py (1)
has_any_quant(2155-2158)tensorrt_llm/quantization/mode.py (1)
has_any_quant(205-221)
tests/unittest/_torch/modules/test_awq_quantization.py (7)
tensorrt_llm/models/modeling_utils.py (2)
PretrainedConfig(369-570)QuantConfig(131-271)tensorrt_llm/_torch/modules/fused_moe/routing.py (1)
DefaultMoeRoutingMethod(188-219)tensorrt_llm/_torch/modules/fused_moe/create_moe.py (1)
create_moe(280-404)tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (3)
ConfigurableMoE(57-1259)backend(899-905)backend(908-937)tensorrt_llm/_torch/model_config.py (1)
torch_dtype(179-184)tensorrt_llm/mapping.py (1)
Mapping(363-542)tensorrt_llm/_torch/distributed/communicator.py (1)
tp_size(64-65)
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (2)
tensorrt_llm/models/modeling_utils.py (1)
QuantConfig(131-271)tensorrt_llm/_torch/modules/fused_moe/create_moe.py (1)
get_moe_cls(24-68)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
🔇 Additional comments (7)
tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py (1)
219-241: LGTM! Guard condition aligns with codebase patterns.The added condition
self.quant_config is not None and self.quant_config.layer_quant_mode.has_any_quant(exclude_kv_cache=True)follows the established pattern frominterface.py(lines 667-670) andlinear.py(lines 2154-2157), ensuring consistent quantization method selection across the codebase.tensorrt_llm/_torch/modules/fused_moe/create_moe.py (1)
345-367: LGTM! Environment variable handling and parameter wiring look correct.The changes correctly:
- Move the environment variable reading into the function scope for runtime flexibility
- Wire the
override_quant_configparameter through toConfigurableMoEThe conditional logic at line 347 appropriately enables ConfigurableMoE when either the env flag is set or when using
CuteDslFusedMoE.tests/unittest/_torch/modules/test_awq_quantization.py (1)
112-149: LGTM! Test correctly adapted to model_config-driven MoE creation.The test changes properly:
- Set up
PretrainedConfigwith required MoE parameters- Create
ModelConfigwithpretrained_config,mapping,quant_config, andmoe_backend- Simplify
create_moecall since parameters are now inferred frommodel_config.pretrained_config- Handle
fc31_act_scaleassignment correctly for bothConfigurableMoE(viamoe.backend) and directTRTLLMGenFusedMoE(viamoe)tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (4)
40-40: LGTM! Import added for QuantConfig type annotation.
136-137: LGTM! Correctly wires override_quant_config to backend class selection.
481-485: LGTM! Workspace sizing correctly accounts for EP-based padding.The update properly calculates
num_rowsasmoe_ep_size * max(all_rank_num_tokens)when using distributed communication. This aligns with the dispatch tensor shape[ep_size * max_tokens_per_rank, ...]as documented in the comment.
752-762: LGTM! Workspace sizing optimizations are correct.The changes properly:
- Account for EP-based padding in
chunk_size_0calculation (line 753)- Reuse
chunk_size_0forworkspace_1sincesplit_chunkguarantees the first chunk is always >= subsequent chunks (the distribution[val_div + 1] * val_mod + [val_div] * restputs larger chunks first)
|
PR_Github #28644 [ run ] triggered by Bot. Commit: |
|
PR_Github #28644 [ run ] completed with state |
|
/bot run --disable-fail-fast |
|
PR_Github #28667 [ run ] triggered by Bot. Commit: |
|
PR_Github #28667 [ run ] completed with state |
|
/bot run --disable-fail-fast |
|
PR_Github #28697 [ run ] triggered by Bot. Commit: |
|
PR_Github #28697 [ run ] completed with state
|
|
/bot run --disable-fail-fast |
|
PR_Github #28749 [ run ] triggered by Bot. Commit: |
|
PR_Github #28749 [ run ] completed with state
|
|
/bot run --disable-fail-fast |
|
/bot run --disable-fail-fast |
|
PR_Github #28837 [ run ] triggered by Bot. Commit: |
|
/bot run --disable-fail-fast |
|
PR_Github #28876 [ run ] triggered by Bot. Commit: |
3 participants
…le_moe
Summary by CodeRabbit
New Features
Bug Fixes
Refactor
✏️ Tip: You can customize this high-level summary in your review settings.
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.
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.
GitHub Bot Help
/bot [-h] ['run', 'kill', 'skip', 'reuse-pipeline'] ...Provide a user friendly way for developers to interact with a Jenkins server.
Run
/bot [-h|--help]to print this help message.See details below for each supported subcommand.
Details
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.