tensorrt_llm/_torch/models/checkpoints/hf/weight_loader.py (1)

74-75: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Use Python 3.10 built-in generics for the new annotation.

Line 74 adds List[str]; prefer list[str] for new Python 3.10+ code.

Proposed fix

-    def _weight_files_cache_key(weight_files: List[str],
+    def _weight_files_cache_key(weight_files: list[str],
                                 use_consolidated: bool) -> tuple:

Based on learnings, Python 3.10+ features are available throughout the codebase. As per coding guidelines, “Prefer using the built-in types list, dict, and tuple.”

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tensorrt_llm/_torch/models/checkpoints/hf/weight_loader.py` around lines 74 -
75, The new type annotation in _weight_files_cache_key still uses typing.List;
update the weight_files parameter to use the Python 3.10 built-in generic
list[str] instead. Keep the rest of the signature unchanged and apply the same
built-in typing style consistently in this function if any related annotations
are added.

Sources: Coding guidelines, Learnings

tests/unittest/_torch/models/checkpoints/hf/test_weight_loader.py (1)

103-103: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Add return annotations to the new test functions.

Line 103 and Line 136 should use -> None.

Proposed fix

-def test_weight_cache_reuses_raw_weights_with_fresh_consumable_wrapper(tmp_path, monkeypatch):
+def test_weight_cache_reuses_raw_weights_with_fresh_consumable_wrapper(tmp_path, monkeypatch) -> None:

-def test_weight_cache_disabled_by_default(tmp_path, monkeypatch):
+def test_weight_cache_disabled_by_default(tmp_path, monkeypatch) -> None:

As per coding guidelines, “Always annotate functions. Make the return type None if the function does not return anything.”

Also applies to: 136-136

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/unittest/_torch/models/checkpoints/hf/test_weight_loader.py` at line
103, Add explicit return type annotations to the new pytest functions in
test_weight_loader, including
test_weight_cache_reuses_raw_weights_with_fresh_consumable_wrapper and the other
new test at the referenced location; both should be annotated with -> None to
match the project’s function annotation guideline. Use the test function names
to locate and update their definitions without changing any test logic.

Source: Coding guidelines

tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)

2585-2593: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Guard against silent coverage loss from duplicate case IDs.

Line 2585 builds a dict keyed by id, so duplicate entries in _NVFP4_4GPU_PREMERGE_CASES would overwrite earlier cases before the set comparison runs. Add a uniqueness assertion before creating cases_by_id.

Proposed check

+        case_ids = tuple(case["id"] for case in self._NVFP4_4GPU_PREMERGE_CASES)
+        assert len(case_ids) == len(set(case_ids)), (
+            "Duplicate DeepSeek V3 Lite NVFP4 premerge case IDs"
+        )
         cases_by_id = {
             case["id"]: case
             for case in self._NVFP4_4GPU_PREMERGE_CASES
         }

As per path instructions, tests/**: act as a QA engineer reviewing test changes and coverage for TensorRT-LLM.

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/integration/defs/accuracy/test_llm_api_pytorch.py` around lines 2585 -
2593, Add a pre-check in the NVFP4 4GPU premerge coverage test to fail fast on
duplicate case IDs before building cases_by_id, since the dict comprehension in
the accuracy test can silently overwrite earlier entries. In the test method
that uses _NVFP4_4GPU_PREMERGE_CASES and _NVFP4_4GPU_PREMERGE_GROUPS, assert
that the list of case["id"] values is unique, then keep the existing
grouped_case_ids coverage assertions after cases_by_id is created.

Source: Path instructions

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes