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[#20511][fix] DeepSeek-V4.1: use BF16 CSA2 projections off SM100/SM103 - #20512
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DeepseekV41Attention always requested native MXFP8 projections, which the CSA2 module only allows on SM100/SM103, so the model failed to build on Hopper even though CSA2 lists an SM90 (FlashMLA BF16) path. Request MXFP8 only on SM100/SM103 and the module's BF16 default elsewhere. Signed-off-by: Han-Yin Chang <nick20350@gmail.com>
…16 load audit With BF16 CSA2 projections the loader folds o_a_proj.weight_scale_inv into the BF16 o_a_proj weight and leaves the o_a_proj_scale buffer None. The V4.1 load-coverage audit takes its targets from named_buffers(), which skips None buffers, so each layer's grouped output scale was reported as a tensor no module would load. Count <attn>.o_a_proj_scale as a target for CSA2 attention with BF16 projections; the native MXFP8 audit is unchanged. The audit test fixture now stands up o_a_proj_scale only for native MXFP8 attention, as a real load does, and the release census test runs from Hopper on. Signed-off-by: Han-Yin Chang <nick20350@gmail.com>
…a GPU The existing projection-mode test reads the real device capability, and this file runs on no Hopper CI stage, so CI would only reach the MXFP8 branch. Mock the capability for SM90, SM100, SM103 and SM120 and record the projection mode the adapter requests from CSA2, so the check runs on a CPU-only host. Signed-off-by: Han-Yin Chang <nick20350@gmail.com>
WalkthroughDeepSeek-V4.1 selects CSA2 projection quantization based on native MXFP8 support. BF16 mode is used otherwise. Weight-load coverage includes the BF16 grouped-output scale target. ChangesDeepSeek-V4.1 CSA2 support
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix · Severity of issue fixed: Medium Sequence Diagram(s)sequenceDiagram
participant DeepseekV41Attention
participant supports_native_mxfp8
participant CSA2Attention
participant WeightLoader
DeepseekV41Attention->>supports_native_mxfp8: Check device and GEMM support
supports_native_mxfp8-->>DeepseekV41Attention: Return MXFP8 or BF16 capability
DeepseekV41Attention->>CSA2Attention: Configure projection quantization
WeightLoader->>CSA2Attention: Load projection weights and scales
CSA2Attention-->>WeightLoader: Fold BF16 scales or retain MXFP8 scale buffer
Suggested reviewers: Merge Risk: 🔵 Low · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
Docstring coverage is 22.22% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 18 functions across 3 files. | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Title check The title clearly identifies the issue, fix type, and primary change: use BF16 CSA2 projections on devices outside SM100/SM103. Description check The description is complete and relevant. It explains the problem, implementation, test coverage, validation results, limitations, documentation impact, and checklist status. Linked Issues check The PR satisfies the coding requirements in [ #20511].DeepseekV41Attentionselectsmxfp8only when CUDA reports SM100 or SM103 andmxfp8_mxfp8_gemmexists. It selectsbf16otherwise. The CSA2 …Out of Scope Changes check The changes stay within [ #20511]. The helper centralizes the hardware and operator check. The projection-mode log, grouped-scale tests, Hopper census coverage, and supported-models documentation suppo…Full details: Linked Issues check
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Explanation
The PR satisfies the coding requirements in [
#20511].DeepseekV41Attentionselectsmxfp8only when CUDA reports SM100 or SM103 andmxfp8_mxfp8_gemmexists. It selectsbf16otherwise. The CSA2 constructor rejects an explicit unsupportedmxfp8request with the existing device or operator errors. BF16 loading foldso_a_proj.weight_scale_invintoo_a_proj;_v41_load_coverageincludeso_a_proj_scalefor BF16 CSA2 attention. The tests cover device selection, constructor errors, grouped-scale coverage, and the Hopper release census. The documentation records the BF16 fallback.
Full details: Out of Scope Changes check
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Explanation
The changes stay within [
#20511]. The helper centralizes the hardware and operator check. The projection-mode log, grouped-scale tests, Hopper census coverage, and supported-models documentation support the compatibility fix. No unrelated production behavior is demonstrated.
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Approving — the comments below are optional touch-ups, not blockers.
Both halves of the fix check out against the loader. csa2/weights.py only assigns o_a_proj_scale on the native path, and the unfed side of _v41_load_coverage iterates parameters only, so the extra BF16 target can't hide a missing source. The H200 before/after table makes the change easy to verify.
Two optional follow-ups:
- The BF16 fallback is silent. On non-SM100/103 GPUs the attention projections are now dequantized to BF16. The weight values stay exact, but those projections use about 2x the weight memory and run a different GEMM. Logging the selected projection mode once at INFO would make that visible when someone compares memory or throughput across GPUs.
- The new behavior on SM120/121 and other SM10x is untested, as the description says. A line about it in the DeepSeek-V4.1 model docs or the supported-models notes would help users on those GPUs.
| sparse_params=params, | ||
| projection_quantization="mxfp8", | ||
| projection_quantization=( | ||
| "mxfp8" if torch.cuda.get_device_capability() in ((10, 0), (10, 3)) else "bf16" |
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This copies the ((10, 0), (10, 3)) check from csa2/module.py:112. If CSA2 later supports native MXFP8 on another SM, this adapter will keep choosing BF16 there with no error to point at the mismatch. Could CSA2 expose the check instead (e.g. a module-level supports_native_mxfp8() that also covers torch.cuda.is_available() and the mxfp8_mxfp8_gemm op check)? Then this line and the constructor's raise would both call it.
The adapter copied the SM100/SM103 check from the CSA2 constructor, so a later change there would leave the adapter choosing BF16 with nothing pointing at the mismatch. Add supports_native_mxfp8() to the CSA2 module (CUDA available, SM100/SM103, mxfp8_mxfp8_gemm compiled) and call it from both the adapter and the constructor. The constructor raises the same errors as before. On SM100/SM103 without the MXFP8 GEMM, the adapter now requests BF16 projections instead of failing with that RuntimeError. The CPU-only test also mocks CUDA availability and the GEMM op, and a new test checks the constructor errors when MXFP8 is requested on a device that cannot run it. Signed-off-by: Han-Yin Chang <nick20350@gmail.com>
Off SM100/SM103 the CSA2 attention projections are dequantized to BF16, which changes weight memory and the GEMM path without any message. Log the selected mode once per process at INFO. Signed-off-by: Han-Yin Chang <nick20350@gmail.com>
…M103 Add a supported-models footnote for the DeepSeek-V4.1 architectures: off SM100/SM103 the CSA2 attention projections run in BF16, and SM120/SM121 and other SM10x GPUs are untested. Signed-off-by: Han-Yin Chang <nick20350@gmail.com>
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Thanks for the review! I've updated the MXFP8 support check, added a log for the selected projection mode, and clarified the BF16 fallback in the docs. |
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@heyuhhh, could you help to review this PR as trt-llm-torch-attention-devs? Thanks~ Assignment explanation
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I checked both earlier follow-ups against the current code, and both are resolved:
- Duplicated SM check: CSA2 now exposes
supports_native_mxfp8()([csa2/module.py:41](https://github.com/NVIDIA/TensorRT-LLM/pull/20512/files#diff-621f4647ebbb4bd1862c6a85d14d00e930f9d3b3cfcc8c85adb1d9f0db8835a5R41)). The adapter calls it at[modeling_deepseekv41.py:133](https://github.com/NVIDIA/TensorRT-LLM/pull/20512/files#diff-9a08809b69e171fc038a4e3de867425fb07e46d88225fa861b643d7f32a1cbf9R133), and the CSA2 constructor raises its errors from the same helper. If CSA2 adds another native SM later, only one tuple needs to change. - Silent BF16 fallback: the selected mode is logged once at INFO (
[modeling_deepseekv41.py:134](https://github.com/NVIDIA/TensorRT-LLM/pull/20512/files#diff-9a08809b69e171fc038a4e3de867425fb07e46d88225fa861b643d7f32a1cbf9R134)), and footnote[^21]insupported-models.mddocuments BF16 projections off SM100/SM103. The CPU test asserts that the log fires exactly once across two layers.
The load-coverage change is still safe. csa2/weights.py:191 sets o_a_proj_scale only on the native path. The new targets are added only for attention modules running BF16, and the unfed side of the audit iterates parameters only, so the extra name cannot hide a missing source. The parametrized test_csa2_grouped_output_scale_passes_load_coverage covers both directions, including the MXFP8 case where the audit must still reject an unset buffer.
The description also covers the other behavior change: SM100/SM103 builds without the MXFP8 GEMM compiled now fall back to BF16 instead of raising RuntimeError.
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Review comments at @tests/unittest/_torch/modeling/test_modeling_deepseekv41.py:
- Around line 3033-3040: Add the existing skip_pre_hopper decorator to
test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103, while
retaining requires_cuda, so the test is skipped before constructing
DeepseekV41Attention on unsupported pre-Hopper devices.
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tests/unittest/_torch/modeling/test_modeling_deepseekv41.py
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| @requires_cuda | ||
| def test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103(): | ||
| model_config, _ = _tiny_v41_model_config() | ||
| attention = DeepseekV41Attention(model_config, layer_idx=0) | ||
| native = torch.cuda.get_device_capability() in ((10, 0), (10, 3)) | ||
| assert attention.projection_quantization == ("mxfp8" if native else "bf16") | ||
| del attention | ||
| torch.cuda.empty_cache() |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🔎 Supported by static analysis
🏁 Script executed:
#!/bin/bash
fd -i 'module.py' --full-path tensorrt_llm/_torch/attention/backends/sparse/csa2 --exec sed -n '1,140p' {}
rg -nP 'get_device_capability|sm_version|get_sm_version' tensorrt_llm/_torch/attention/backends/sparse/csa2
rg -n 'test_modeling_deepseekv41' tests/integration/test_lists/test-db/Repository: NVIDIA/TensorRT-LLM
Length of output: 8931
🏁 Script executed:
set -eu
printf '%s\n' '--- test imports and target test ---'
sed -n '1,90p' tests/unittest/_torch/modeling/test_modeling_deepseekv41.py
sed -n '3000,3055p' tests/unittest/_torch/modeling/test_modeling_deepseekv41.py
printf '%s\n' '--- DeepseekV41Attention definitions and callers ---'
rg -n -F --glob '*.py' -e 'class DeepseekV41Attention' -e 'DeepseekV41Attention(' -e 'select_csa2_backend(' tensorrt_llm tests/unittest/_torch/modeling/test_modeling_deepseekv41.py
printf '%s\n' '--- constructor continuation and backend call ---'
sed -n '90,260p' tensorrt_llm/_torch/attention/backends/sparse/csa2/module.py
sed -n '120,180p' tensorrt_llm/_torch/attention/backends/sparse/csa2/backend.py
sed -n '100,155p' tensorrt_llm/_torch/attention/backends/sparse/csa2/indexer.py
printf '%s\n' '--- changed test diff against supplied base ---'
git diff --unified=30 4636b6311f55ec4ffe16d44fba3601f41638ec42 b43f1285c7bc1f2d68ad2b9ec03f255b38f93313 -- tests/unittest/_torch/modeling/test_modeling_deepseekv41.pyRepository: NVIDIA/TensorRT-LLM
Length of output: 39947
🏁 Script executed:
set -eu
printf '%s\n' '--- model adapter construction ---'
nl -ba tensorrt_llm/_torch/models/modeling_deepseekv41.py | sed -n '115,190p'
printf '%s\n' '--- CSA2 backend selection ---'
nl -ba tensorrt_llm/_torch/attention/backends/sparse/csa2/module.py | sed -n '145,180p'
nl -ba tensorrt_llm/_torch/attention/backends/sparse/csa2/backend.py | sed -n '145,170p'
nl -ba tensorrt_llm/_torch/attention/backends/sparse/csa2/params.py | sed -n '180,205p'
printf '%s\n' '--- SM version helper and marker ---'
rg -n -F -- 'def get_sm_version' tensorrt_llm/_utils.py tensorrt_llm
rg -n -F -- 'def skip_pre_hopper' testsRepository: NVIDIA/TensorRT-LLM
Length of output: 9072
Skip the real-device projection test before Hopper.
DeepseekV41Attention constructs the TRTLLM CSA2 backend before it checks projection_quantization. That backend calls select_csa2_backend(get_sm_version()), which raises for unsupported SM versions. @requires_cuda alone allows pre-Hopper devices, so the test can fail during construction instead of testing projection selection.
🐛 Suggested fix
+@skip_pre_hopper
@requires_cuda
def test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103():Coverage summary: this file adds real-device projection selection coverage, mocked capability and operator-gating cases, CSA2 constructor error cases, grouped output-scale/load-coverage tests, and release-census coverage. The module is listed in the B200, GB200, GB300, and B300 test lists. The real-device test still lacks the required pre-Hopper capability guard.
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| @requires_cuda | |
| def test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103(): | |
| model_config, _ = _tiny_v41_model_config() | |
| attention = DeepseekV41Attention(model_config, layer_idx=0) | |
| native = torch.cuda.get_device_capability() in ((10, 0), (10, 3)) | |
| assert attention.projection_quantization == ("mxfp8" if native else "bf16") | |
| del attention | |
| torch.cuda.empty_cache() | |
| @skip_pre_hopper | |
| @requires_cuda | |
| def test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103(): | |
| model_config, _ = _tiny_v41_model_config() | |
| attention = DeepseekV41Attention(model_config, layer_idx=0) | |
| native = torch.cuda.get_device_capability() in ((10, 0), (10, 3)) | |
| assert attention.projection_quantization == ("mxfp8" if native else "bf16") | |
| del attention | |
| torch.cuda.empty_cache() |
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Review comment at @tests/unittest/_torch/modeling/test_modeling_deepseekv41.py
around lines 3033 - 3040:
Add the existing skip_pre_hopper decorator to
test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103, while
retaining requires_cuda, so the test is skipped before constructing
DeepseekV41Attention on unsupported pre-Hopper devices.
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review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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| def _is_native_mxfp8_sm() -> bool: |
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Could we put this into some place where can be also used by other cases? Or there might be some similar functions somewhere. I believe that this is a very common function and it's better to unify it.
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Thanks for the suggestion! I checked the existing helpers, but none does exactly the same check. I'll keep it in CSA2 for now to keep this PR small.
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Approved with one minor comment. Feel free to resolve it~
Description
Fixes #20511. DeepSeek-V4.1-Flash does not start on SM90:
DeepseekV41Attentionalways passesprojection_quantization="mxfp8", and CSA2 allows MXFP8 projections only on SM100/SM103.DeepseekV41Attentionasks for"mxfp8"only when CSA2'ssupports_native_mxfp8()is true (CUDA available,device capability (10, 0) or (10, 3),
mxfp8_mxfp8_gemmcompiled), and for CSA2's default"bf16"otherwise.The CSA2 constructor uses the same check before raising.
csa2/weights.pydequantizeswo_awith itsweight_scale_invinto the BF16o_a_projand leaves the
o_a_proj_scalebufferNone._v41_load_coveragebuilds its targets fromnamed_buffers(),which skips
Nonebuffers, soassert_load_completerejected the 40 grouped output scales (one per layer).For CSA2 attention running BF16 projections,
<attn>.o_a_proj_scalenow counts as a target.Following review, the selected projection mode is logged once per process at INFO, and
supported-models.mdnotes the BF16projections off SM100/SM103.
On SM100/SM103 with the MXFP8 GEMM compiled, both changes do nothing: the attention is still MXFP8, and the added
target set is empty. On SM100/SM103 without the GEMM compiled, the attention now gets BF16 projections instead of
RuntimeError: CSA2 native MXFP8 GEMM is not compiled. On theother GPUs CSA2 lists (SM120/121, other SM10x) the model now also gets BF16 projections instead of the ValueError;
I have not run those.
Test Coverage
test_attention_uses_native_mxfp8_projections_only_on_sm100_and_sm103(GPU): the projection mode follows thedevice.
test_attention_projection_quantization_follows_device_capability(CPU): the same choice with the capabilitymocked as (9, 0), (10, 0), (10, 3) and (12, 0), with CUDA availability and the GEMM op mocked too, and a check of
the INFO log line; CSA2's constructor is replaced by a recorder, since the module can only be built on a GPU.
test_csa2_rejects_native_mxfp8_projections_the_device_cannot_run(CPU): requesting MXFP8 on a device that cannotrun it still raises.
test_csa2_grouped_output_scale_passes_load_coverage[bf16|mxfp8](CPU): real checkpoint remap, attentionload_weightsand_v41_load_coveragefor onewo_apair. BF16 checks the scale is folded into the weight and theaudit passes; MXFP8 checks the audit still rejects the scale if the buffer is not set.
test_release_census_closes_and_coverage_is_emptyis no longer skipped on Hopper (no Hopper L0 stage runs thisfile today).
_audited_loaderstands upo_a_proj_scaleonly for MXFP8 attention, as a real load does; before, it also set it for BF16 attention.On one H200 (
release:1.4.0rc1.dev202610140007; itsmodeling_deepseekv41.pyis identical to main's, and eachcolumn binds main's file, main + change 1, or main + both over it; the test file of the second commit, before the
mocked-capability test, in every column):
CSA2 native MXFP8 projections require SM100/SM103)40 checkpoint tensor(s) no module would load, e.g.model.layers.0.self_attn.o_a_proj.weight_scale_invtest_modeling_deepseekv41.pyThe 25 skipped tests are the same in both runs; all are skipped as pre-Blackwell.
On a CPU-only host (same image, no GPU visible), the mocked-capability test fails for (9, 0) and (12, 0) on main
(
'mxfp8' != 'bf16') and passes for (10, 0) and (10, 3); with change 1 only, or both, all four pass. The whole filewith both changes on that host: 188 passed, 39 skipped. With the review follow-ups, on a CPU-only host with the same
image: 192 passed, 39 skipped. The H200 runs in this description predate the review follow-ups; I
have not rerun them on a GPU.
End to end on 8 x H200 (TP8, EP8, the
TestDeepSeekV41FlashDSpark.test_gsm8k_tp4settings without DSpark,allreduce_strategy: NCCL), same image with both changes: the server became ready, and the load audit loggedDeepSeek-V4.1 load coverage: 139495 forwarded + 0 synthesized keys all had a consumer; every model parameter had a source.GSM8K, first 100 testquestions, with my own client following the reference test's chat protocol (not lm-eval): 0.97 (97/100). Passkey retrieval: 4/4 at 6774 prompt tokens (control 4/4
at 342). The unchanged model failed at construction in the same job. Weights were loaded through a read-only mmap
because of this cluster's memory overcommit setting. After the clients finished, while the server was shutting down,
all 8 ranks received
SIGBUS(Signal: Bus error (7),Non-existent physical address); I have not looked into thecause yet.
(The release census test needs
LLM_MODELS_ROOTwith theDeepSeek-V4.1-Flashcheckpoint; it reads only thesafetensors headers.)
Not run on hardware: SM100/SM103 (by reading, both changes are no-ops there when the MXFP8 GEMM is compiled), SM120/121, DSpark, the NVFP4
checkpoint, other parallel layouts, performance.
PR Checklist
Dev Engineer Review
DeepSeek-V4.1 selects MXFP8 projections only on supported SM100/SM103 devices with the compiled MXFP8 GEMM operator. It uses BF16 projections otherwise and logs the selected mode once per process.
BF16
wo_ascales are folded intoo_a_proj. Load-coverage auditing now includes the corresponding scale target. Documentation records the BF16 fallback and untested GPU generations. Other hardware, layouts, and performance remain unverified.QA Engineer Review
tests/unittest/_torch/modeling/test_modeling_deepseekv41.pyadds coverage for projection selection, unsupported-device and missing-operator errors, grouped-output scale folding, load coverage, and release-census behavior. The release-census skip changes from pre-Blackwell to pre-Hopper.The test file is listed in four CI test lists:
l0_gb200_multi_gpus.yml,l0_b200.yml,l0_gb300.yml, andl0_b300.yml. No manual-QA entry was identified.Reported validation includes CPU tests with 192 passed and 39 skipped, plus 8×H200 server and inference validation. GPU tests were not rerun after follow-up changes. Coverage verdict: needs follow-up for untested GPU generations, layouts, and performance.
Per-File QA Perspective
tensorrt_llm/_torch/models/modeling_deepseekv41.py— Verify runtime MXFP8/BF16 selection, one-time logging, and BF16 scale load coverage.tensorrt_llm/_torch/attention/backends/sparse/csa2/module.py— Verify device-capability and compiled-operator checks, including expected unsupported-setup errors.tests/unittest/_torch/modeling/test_modeling_deepseekv41.py— Covers projection selection, constructor errors, scale folding, and load coverage. It is listed in four CI test lists. No manual-QA entry was identified.docs/source/models/supported-models.md— Verify that documented BF16 fallback behavior matches implementation and supported hardware.