[TRTLLM-5252][fix] Propagate mapping to intermediate layers (#6611) #6765
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) This commit propagates the mapping to intermediate layers to enable tensor parallelism (amongst other things) in them. It also fixes issues with a unit test for TP for pixtral, and adds it to a test list. Signed-off-by: William Zhang <[email protected]>
📝 WalkthroughWalkthroughThis change updates the initialization of Changes
Sequence Diagram(s)sequenceDiagram
participant MainProcess
participant WorkerProcess
MainProcess->>WorkerProcess: Spawn with mapping argument
WorkerProcess->>WorkerProcess: Call make_pixtral_vision_config()
WorkerProcess->>WorkerProcess: Assign mapping to config
WorkerProcess->>WorkerProcess: Run test logic with config
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📒 Files selected for processing (3)
tensorrt_llm/_torch/models/modeling_mistral.py(2 hunks)tests/integration/test_lists/test-db/l0_dgx_h100.yml(1 hunks)tests/unittest/_torch/modeling/test_modeling_pixtral.py(6 hunks)
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Files:
tensorrt_llm/_torch/models/modeling_mistral.pytests/unittest/_torch/modeling/test_modeling_pixtral.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
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All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tensorrt_llm/_torch/models/modeling_mistral.pytests/unittest/_torch/modeling/test_modeling_pixtral.py
🧠 Learnings (5)
📓 Common learnings
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.
📚 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/test_lists/test-db/l0_dgx_h100.ymltests/unittest/_torch/modeling/test_modeling_pixtral.py
📚 Learning: 2025-08-06T13:58:07.506Z
Learnt from: galagam
PR: NVIDIA/TensorRT-LLM#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:
tests/integration/test_lists/test-db/l0_dgx_h100.yml
📚 Learning: 2025-08-06T03:47:16.802Z
Learnt from: venkywonka
PR: NVIDIA/TensorRT-LLM#6650
File: tests/integration/test_lists/qa/llm_perf_cluster.yml:33-37
Timestamp: 2025-08-06T03:47:16.802Z
Learning: Ministral is a valid model name from Mistral AI, distinct from the regular Mistral models. In TensorRT-LLM test configurations, "ministral_8b" and "ministral_8b_fp8" are correct model identifiers and should not be changed to "mistral_8b".
Applied to files:
tensorrt_llm/_torch/models/modeling_mistral.py
📚 Learning: 2025-08-06T03:47:16.802Z
Learnt from: venkywonka
PR: NVIDIA/TensorRT-LLM#6650
File: tests/integration/test_lists/qa/llm_perf_cluster.yml:33-37
Timestamp: 2025-08-06T03:47:16.802Z
Learning: Ministral is a valid and distinct model family from Mistral AI, separate from their regular Mistral models. Ministral 8B is specifically designed for edge computing and on-device applications, released in October 2024. In TensorRT-LLM test configurations, "ministral_8b" and "ministral_8b_fp8" are correct model identifiers and should not be changed to "mistral_8b".
Applied to files:
tensorrt_llm/_torch/models/modeling_mistral.py
🧬 Code Graph Analysis (1)
tensorrt_llm/_torch/models/modeling_mistral.py (1)
tensorrt_llm/_torch/modules/linear.py (1)
Linear(1445-1643)
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🔇 Additional comments (8)
tensorrt_llm/_torch/models/modeling_mistral.py (3)
473-479: Propagate mapping into PatchMerger Linear (good), but verify weight-loading expectationsPassing
mapping=model_config.mappingis correct and aligns with enabling TP awareness in this intermediate layer. One caveat:Mistral3VLM.load_weights()currently callsself._multi_modal_projector.load_state_dict(...)(not sharded). If you later switch to sharded loading for the projector stack, confirm that this merger layer’s TP mode and weight shapes line up with the sharding logic; otherwise you might unknowingly replicate weights across ranks.
539-545: Mapping propagation into projector linear_1 looks goodThis ensures projector layers receive distributed context. No functional regressions apparent.
546-553: Mapping propagation into projector linear_2 looks goodSame rationale as above; consistent with the rest of the model.
tests/integration/test_lists/test-db/l0_dgx_h100.yml (1)
57-57: Add pixtral TP test to PyTorch cluster suite (LGTM)Placement under
backend: pytorchwith H100 4-GPU gating is appropriate.tests/unittest/_torch/modeling/test_modeling_pixtral.py (4)
31-41: Replace fixture with config factory (sane change)Local factory avoids pickling issues and keeps tests explicit and self-contained.
73-81: HF parity test refactor uses factory (LGTM)Switching to
make_pixtral_vision_config()is straightforward and correct.Also applies to: 76-81
114-121: TP test setup looks correct (world_size gating, dtype/device, HF weight export)The preparation and cleanup steps are sound.
161-163: Good: avoid sending unpickleable config across processesClear comment and correct strategy: recreate config in worker.
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PR_Github #14666 [ run ] completed with state |
Summary by CodeRabbit
Bug Fixes
Tests
Refactor
[TRTLLM-5252][fix] Propagate mapping to intermediate layers (#6611)
Description
This is a replica of #6611 for
release/1.0.This commit propagates the mapping to intermediate layers to enable tensor parallelism (amongst other things) in them.
It also fixes issues with a unit test for TP for pixtral, and adds it to a test list.
Test Coverage
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