Fix FLOPs inconsistency and add using_sparse_model flag

[Devesh-Maheshwari]

What does this PR do?

Fixes #21677.

The FLOPs values in _CUDA_FLOPS for Hopper GPUs were inconsistent: h100 sxm and h100 pcie used dense per-GPU peaks, while h100 nvl and h200 sxm1 used the headline "with-sparsity" values from NVIDIA's datasheet. This caused a 2× difference in MFU when switching between GPU types.

Changes

Data fix (standardize Hopper on dense):

• h100 nvl — replaced sparse values (which were duplicates of h100 sxm's sparse numbers) with the correct H100 NVL dense per-GPU peaks: 30 / 60 / 417.5 / 835.5 / 835.5 / 1670.5 TFLOPS.
• h200 sxm1 — tensor-core entries now stored as dense halves (494.5 / 989.5 / 989.5 / 1979 TFLOPS) instead of sparse headline values.
• h100 sxm — rounded entries to exact datasheet ÷ 2 (sub-1% drift only).
• Sources verified against H100 and H200 datasheets.

API addition (per agreed design in issue thread):

• Throughput.__init__ now accepts using_sparse_model: Optional[bool] = None and sparse_cuda_acceleration_factor: float = 2.0.
• None (default): no scaling, emits rank_zero_warn so users explicitly choose. True: scales available_flops by the factor. False: dense default, no warning.
• Forwarded automatically through ThroughputMonitor via **kwargs.

Tests

Four new tests in tests/tests_fabric/utilities/test_throughput.py:

• test_throughput_sparse_model_scaling — constructor scales correctly for True / False / None-peak / invalid factor.
• test_throughput_sparse_model_warning — warning fires only when unset + peak known; silenced by explicit True/False or available_flops=None.
• test_throughput_sparse_model_mfu — end-to-end: MFU halves when sparse flag enabled with default 2× factor.
• test_throughput_monitor_sparse_model — ThroughputMonitor forwards both kwargs into Throughput.

All existing tests in the file still pass (37 passing + 5 pre-existing xfails unrelated to this change).

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Codecov Report

✅ All modified and coverable lines are covered by tests.
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Additional details and impacted files

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[deependujha]

Hi @Devesh-Maheshwari, thanks for the great work. Added couple of reviews, please have a look. Thanks :)

[deependujha]

thanks for the great work. :)

[deependujha]

please update changelog file too.

[deependujha]

Thanks @Devesh-Maheshwari, LGTM. :)

[Copilot]

Pull request overview

This PR addresses inconsistent GPU peak FLOPs values used for MFU calculations (notably for Hopper GPUs) and introduces an explicit API to opt into sparsity-accelerated peak FLOPs when computing MFU in Fabric’s throughput utilities.

Changes:

• Standardizes Hopper entries in _CUDA_FLOPS to use dense peak FLOPs (and normalizes FLOPs constants to consistent scientific notation).
• Adds using_sparse_model: Optional[bool] and sparse_cuda_acceleration_factor to Throughput.__init__, with optional scaling of available_flops when sparsity is enabled.
• Adds tests for sparse scaling/warnings/MFU behavior and updates the Fabric changelog.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.

File	Description
src/lightning/fabric/utilities/throughput.py	Adds sparsity-aware FLOPs scaling/warning to Throughput and corrects/normalizes _CUDA_FLOPS entries (especially Hopper).
tests/tests_fabric/utilities/test_throughput.py	Adds tests covering sparse scaling, warning behavior, MFU impact, and ThroughputMonitor kwarg forwarding.
src/lightning/fabric/CHANGELOG.md	Documents the new Throughput parameters and _CUDA_FLOPS changes (needs wording adjustment).

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