[None][fix] default disable gemm+allreduce fusion #10656
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📝 WalkthroughWalkthroughThe pull request changes the default state of GEMM all-reduce fusion from enabled to disabled by modifying the environment variable TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED default behavior in the Llama model, and adds test coverage to validate both enabled and disabled configurations. Changes
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⚠️ Outside diff range comments (1)
tensorrt_llm/_torch/models/modeling_llama.py (1)
1-3: Add SPDX/copyright header to this production.pyfile (required by repo guidelines).
As per coding guidelines, source files undertensorrt_llm/should carry the NVIDIA copyright/SPDX header.Proposed fix
+# +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# import copy import os from typing import Any, Dict, List, Optional, Tuple, Union
🤖 Fix all issues with AI agents
In `@tests/integration/defs/accuracy/test_llm_api_pytorch.py`:
- Around line 696-705: The test function test_fp4_tp2pp2 is missing the param
enable_gemm_allreduce_fusion in its signature and mutates os.environ directly;
update the test_fp4_tp2pp2 signature to accept enable_gemm_allreduce_fusion (in
addition to torch_compile) and replace direct os.environ assignments with
monkeypatch.setenv calls to set "TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED" to "1" or
"0" so the environment is isolated and auto-restored after the test.
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📒 Files selected for processing (2)
tensorrt_llm/_torch/models/modeling_llama.pytests/integration/defs/accuracy/test_llm_api_pytorch.py
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📓 Path-based instructions (2)
**/*.py
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**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+
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Files:
tensorrt_llm/_torch/models/modeling_llama.pytests/integration/defs/accuracy/test_llm_api_pytorch.py
**/*.{cpp,cc,cxx,h,hpp,hxx,cu,cuh,py}
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All TensorRT-LLM source files (.cpp, .h, .cu, .py, and other source files) should contain an NVIDIA copyright header with the year of latest meaningful modification
Files:
tensorrt_llm/_torch/models/modeling_llama.pytests/integration/defs/accuracy/test_llm_api_pytorch.py
🧠 Learnings (16)
📓 Common learnings
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device allreduce implementation (cpp/tensorrt_llm/thop/allreduceOp.cpp), the goto pattern in runNCCLAllReduceDeviceFusion is intentionally used for future extensibility, allowing multiple switch cases to fallback to the default handler. While not aesthetically ideal, this pattern supports adding more fusion cases later that can reuse the same fallback logic.
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
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.
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.
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 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.
Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.
📚 Learning: 2025-09-23T15:12:38.312Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device allreduce implementation (cpp/tensorrt_llm/thop/allreduceOp.cpp), the goto pattern in runNCCLAllReduceDeviceFusion is intentionally used for future extensibility, allowing multiple switch cases to fallback to the default handler. While not aesthetically ideal, this pattern supports adding more fusion cases later that can reuse the same fallback logic.
Applied to files:
tensorrt_llm/_torch/models/modeling_llama.pytests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-14T06:36:40.701Z
Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
Applied to files:
tensorrt_llm/_torch/models/modeling_llama.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/models/modeling_llama.py
📚 Learning: 2025-08-14T15:38:01.771Z
Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: cpp/tensorrt_llm/pybind/thop/bindings.cpp:55-57
Timestamp: 2025-08-14T15:38:01.771Z
Learning: In TensorRT-LLM Python bindings, tensor parameter collections like mla_tensor_params and spec_decoding_tensor_params are kept as required parameters without defaults to maintain API consistency, even when it might affect backward compatibility.
Applied to files:
tensorrt_llm/_torch/models/modeling_llama.py
📚 Learning: 2025-09-23T15:12:38.312Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.
Applied to files:
tensorrt_llm/_torch/models/modeling_llama.py
📚 Learning: 2025-09-09T09:40:45.658Z
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
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:
tensorrt_llm/_torch/models/modeling_llama.pytests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-06T13:58:07.506Z
Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.
Applied to files:
tensorrt_llm/_torch/models/modeling_llama.pytests/integration/defs/accuracy/test_llm_api_pytorch.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/models/modeling_llama.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:
tensorrt_llm/_torch/models/modeling_llama.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:
tensorrt_llm/_torch/models/modeling_llama.py
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 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
📚 Learning: 2025-08-26T09:49:04.956Z
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 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/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-11-27T09:23:18.742Z
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-11T20:09:24.389Z
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.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/integration/defs/accuracy/test_llm_api_pytorch.py
🧬 Code graph analysis (1)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)
tests/integration/defs/conftest.py (1)
parametrize_with_ids(1836-1862)
🪛 Ruff (0.14.11)
tests/integration/defs/accuracy/test_llm_api_pytorch.py
701-701: Undefined name enable_gemm_allreduce_fusion
(F821)
⏰ 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 (1)
tensorrt_llm/_torch/models/modeling_llama.py (1)
677-690: Default-disable GEMM+allreduce fusion looks consistent with the stated rollout.
This flips the default to disabled while still allowing explicit opt-in viaTRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED=1.
✏️ Tip: You can disable this entire section by setting review_details to false in your review settings.
Comment on lines
696
to
705
| @parametrize_with_ids("enable_gemm_allreduce_fusion", [True, False]) | ||
| def test_fp4_tp2pp2(self, torch_compile): | ||
| model_path = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP4" | ||
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5) | ||
| torch_compile_config = _get_default_torch_compile_config(torch_compile) | ||
| if enable_gemm_allreduce_fusion: | ||
| os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "1" | ||
| else: | ||
| os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "0" | ||
|
|
Contributor
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🧩 Analysis chain
🏁 Script executed:
# Find and read the test file around lines 696-705
find . -path "*/tests/integration/defs/accuracy/test_llm_api_pytorch.py" -type fRepository: NVIDIA/TensorRT-LLM
Length of output: 120
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wc -l tests/integration/defs/accuracy/test_llm_api_pytorch.pyRepository: NVIDIA/TensorRT-LLM
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sed -n '680,730p' tests/integration/defs/accuracy/test_llm_api_pytorch.pyRepository: NVIDIA/TensorRT-LLM
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# Check if monkeypatch is used elsewhere in the file
rg "monkeypatch" tests/integration/defs/accuracy/test_llm_api_pytorch.pyRepository: NVIDIA/TensorRT-LLM
Length of output: 204
Add missing enable_gemm_allreduce_fusion parameter to method signature and use monkeypatch to isolate environment variable changes.
The parametrize decorator references enable_gemm_allreduce_fusion, but the method signature only accepts torch_compile. This will cause a NameError when the method tries to reference the undefined variable on line 702. Additionally, directly modifying os.environ without cleanup can leak state to other tests. Use monkeypatch.setenv() instead, which auto-restores the environment after the test completes.
Proposed fix
`@pytest.mark.skip_less_device`(4)
`@skip_pre_blackwell`
`@parametrize_with_ids`("torch_compile", [False, True])
`@parametrize_with_ids`("enable_gemm_allreduce_fusion", [True, False])
- def test_fp4_tp2pp2(self, torch_compile):
+ def test_fp4_tp2pp2(self, torch_compile, enable_gemm_allreduce_fusion,
+ monkeypatch):
model_path = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP4"
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5)
torch_compile_config = _get_default_torch_compile_config(torch_compile)
- if enable_gemm_allreduce_fusion:
- os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "1"
- else:
- os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "0"
+ monkeypatch.setenv(
+ "TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED",
+ "1" if enable_gemm_allreduce_fusion else "0",
+ )
with LLM(model_path,
tensor_parallel_size=2,
pipeline_parallel_size=2,
max_batch_size=32,
kv_cache_config=kv_cache_config,
torch_compile_config=torch_compile_config) as llm:🧰 Tools
🪛 Ruff (0.14.11)
701-701: Undefined name enable_gemm_allreduce_fusion
(F821)
🤖 Prompt for AI Agents
In `@tests/integration/defs/accuracy/test_llm_api_pytorch.py` around lines 696 -
705, The test function test_fp4_tp2pp2 is missing the param
enable_gemm_allreduce_fusion in its signature and mutates os.environ directly;
update the test_fp4_tp2pp2 signature to accept enable_gemm_allreduce_fusion (in
addition to torch_compile) and replace direct os.environ assignments with
monkeypatch.setenv calls to set "TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED" to "1" or
"0" so the environment is isolated and auto-restored after the test.
Collaborator
There was a problem hiding this comment.
This comment by CodeRabbit should be followed :) you can use the built-in libraries as well:
with mock.patch.dict(
os.environ,
{"TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED": str(int(enable_gemm_allreduce_fusion))},
), LLM(...):
# etc.
Contributor
There was a problem hiding this comment.
@2ez4bz Thank you for the suggestion! Using mock.patch.dict is indeed a cleaner approach. Here's the complete fix including the missing parameter in the function signature:
`@pytest.mark.skip_less_device`(4)
`@skip_pre_blackwell`
`@parametrize_with_ids`("torch_compile", [False, True])
`@parametrize_with_ids`("enable_gemm_allreduce_fusion", [True, False])
def test_fp4_tp2pp2(self, torch_compile, enable_gemm_allreduce_fusion):
model_path = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP4"
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5)
torch_compile_config = _get_default_torch_compile_config(torch_compile)
with mock.patch.dict(
os.environ,
{"TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED": str(int(enable_gemm_allreduce_fusion))},
), LLM(
model_path,
tensor_parallel_size=2,
pipeline_parallel_size=2,
max_batch_size=32,
kv_cache_config=kv_cache_config,
torch_compile_config=torch_compile_config
) as llm:
assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4
sampling_params = SamplingParams(
max_tokens=256,
temperature=0.0,
add_special_tokens=False,
)
task = MMLU(self.MODEL_NAME)
task.evaluate(llm, sampling_params=sampling_params)
task = GSM8K(self.MODEL_NAME)
task.evaluate(llm, sampling_params=sampling_params)
task = GPQADiamond(self.MODEL_NAME)
task.evaluate(llm,
extra_evaluator_kwargs=dict(apply_chat_template=True))Note: Make sure mock is imported at the top of the file:
from unittest import mockThis approach is better because:
- No need for the
monkeypatchfixture parameter - The environment variable change is scoped to just this test execution
- Automatic cleanup via context managers
- More explicit and readable
🧠 Learnings used
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 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.
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
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.
Learnt from: nvpohanh
Repo: NVIDIA/TensorRT-LLM PR: 7478
File: tests/unittest/_torch/modeling/test_modeling_llama_min_latency.py:286-308
Timestamp: 2025-09-03T13:16:38.028Z
Learning: In test files, temporary monkey-patches for upstream bugs can be kept simple when they are explicitly intended to be removed soon, rather than investing effort in making them more robust.
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.
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 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*").
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.
Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device allreduce implementation (cpp/tensorrt_llm/thop/allreduceOp.cpp), the goto pattern in runNCCLAllReduceDeviceFusion is intentionally used for future extensibility, allowing multiple switch cases to fallback to the default handler. While not aesthetically ideal, this pattern supports adding more fusion cases later that can reuse the same fallback logic.
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.
benzh-2025
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| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5) | ||
| torch_compile_config = _get_default_torch_compile_config(torch_compile) | ||
| if enable_gemm_allreduce_fusion: | ||
| os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "1" |
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Multiple tests can run in same process, so you'll need to clean up the env after the test run.
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good point, let me change, thanks a lot.
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| @parametrize_with_ids("enable_gemm_allreduce_fusion", [True, False]) | ||
| def test_fp4_tp2pp2(self, torch_compile): | ||
| model_path = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP4" | ||
| kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5) | ||
| torch_compile_config = _get_default_torch_compile_config(torch_compile) | ||
| if enable_gemm_allreduce_fusion: | ||
| os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "1" | ||
| else: | ||
| os.environ["TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED"] = "0" | ||
|
|
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This comment by CodeRabbit should be followed :) you can use the built-in libraries as well:
with mock.patch.dict(
os.environ,
{"TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED": str(int(enable_gemm_allreduce_fusion))},
), LLM(...):
# etc.
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byshiue
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Signed-off-by: benzh-2025 <[email protected]>
Signed-off-by: benzh-2025 <[email protected]>
Signed-off-by: benzh-2025 <[email protected]>
Signed-off-by: benzh-2025 <[email protected]>
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@coderabbitai summary
Description
Test Coverage
GitHub Bot Help
/bot [-h] ['run', 'kill', 'skip', 'reuse-pipeline'] ...Provide a user friendly way for developers to interact with a Jenkins server.
Run
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