[OMNIML-2336][feat] w4a8 nvfp4 fp8 exports scale factor properly #8180
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Now, Modelopt will export its scale factor in range 448/6. Signed-off-by: Shiyang Chen <[email protected]>
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📝 WalkthroughWalkthroughRemoved the aggregated NVFP4 weights-and-alphas loader in MoE quantization, adjusted linear FP8/NVFP4 scaling logic to drop 6x normalization and assertion, and enabled a previously skipped FP4 MoE test. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant Caller
participant NVFP4FusedMoEMethod
participant ExpertLoaders as Per-Expert Loaders
Note over NVFP4FusedMoEMethod: New flow (aggregated loader removed)
Caller->>NVFP4FusedMoEMethod: load_quant_scales(module, weights, expert_ids, ...)
alt Per-expert scale loading
NVFP4FusedMoEMethod->>ExpertLoaders: load w3_w1 scales per expert
NVFP4FusedMoEMethod->>ExpertLoaders: load w2 scales per expert
Note right of ExpertLoaders: No consolidated alphas/scales post-processing
end
NVFP4FusedMoEMethod-->>Caller: scales/aligned tensors
sequenceDiagram
autonumber
participant PyTest
participant TestSuite as tests/unittest/_torch/thop/parallel/test_moe.py
Note over TestSuite: TestMoeFP4 previously skipped
PyTest->>TestSuite: collect tests
Note over PyTest,TestSuite: Skip marker removed
PyTest->>TestSuite: execute TestMoeFP4
TestSuite-->>PyTest: report result
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
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📝 WalkthroughWalkthroughRemoved NVFP4 MoE helper for loading FP4 scales/alphas, eliminated 6.0 scaling adjustments in NVFP4FP8 weight scale handling, and re-enabled a previously skipped FP4 MoE autotune test. Changes
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tensorrt_llm/_torch/modules/fused_moe/quantization.py(0 hunks)tensorrt_llm/_torch/modules/linear.py(1 hunks)tests/unittest/_torch/thop/parallel/test_moe.py(0 hunks)
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📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.py: Python code must target Python 3.8+.
Indent Python code with 4 spaces; do not use tabs.
Maintain module namespace when importing; prefer 'from package.subpackage import foo' then 'foo.SomeClass()' instead of importing the class directly.
Python filenames should be snake_case (e.g., some_file.py).
Python classes use PascalCase names.
Functions and methods use snake_case names.
Local variables use snake_case; prefix 'k' for variables that start with a number (e.g., k_99th_percentile).
Global variables use upper SNAKE_CASE prefixed with 'G' (e.g., G_MY_GLOBAL).
Constants use upper SNAKE_CASE (e.g., MY_CONSTANT).
Avoid shadowing variables from an outer scope.
Initialize all externally visible members of a class in the constructor.
Prefer docstrings for interfaces that may be used outside a file; comments for in-function or file-local interfaces.
Use Google-style docstrings for classes and functions (Sphinx-parsable).
Document attributes and variables inline so they render under the class/function docstring.
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In try/except, catch the most specific exceptions possible.
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remove the pytest skip. Signed-off-by: Shiyang Chen <[email protected]>
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…DIA#8180) Signed-off-by: Shiyang Chen <[email protected]> Co-authored-by: Shiyang Chen <[email protected]>
…DIA#8180) Signed-off-by: Shiyang Chen <[email protected]> Co-authored-by: Shiyang Chen <[email protected]> Signed-off-by: yufeiwu-nv <[email protected]>
…DIA#8180) Signed-off-by: Shiyang Chen <[email protected]> Co-authored-by: Shiyang Chen <[email protected]>
…DIA#8180) Signed-off-by: Shiyang Chen <[email protected]> Co-authored-by: Shiyang Chen <[email protected]>
…DIA#8180) Signed-off-by: Shiyang Chen <[email protected]> Co-authored-by: Shiyang Chen <[email protected]>
…DIA#8180) Signed-off-by: Shiyang Chen <[email protected]> Co-authored-by: Shiyang Chen <[email protected]>
3 participants
Now, Modelopt will export its scale factor in range 448/6.
This PR goes together with NVIDIA/TensorRT-Model-Optimizer#406.
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