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WalkthroughGemma4 attention now resolves heterogeneous per-layer head dimensions and KV-head counts through shared helpers. KV-cache sizing and attention metadata use the same geometry. Backend defaults now depend on Blackwell architecture and external shared-KV MTP requirements. ChangesGemma4 attention geometry
KV-cache sizing
Attention metadata
Backend defaults
Estimated code review effort: 3 (Moderate) | ~25 minutes Sequence Diagram(s)sequenceDiagram
participant Gemma4Model
participant config_utils
participant KVCacheManagerV2
participant ModelEngine
Gemma4Model->>config_utils: resolve per-layer attention geometry
Gemma4Model->>KVCacheManagerV2: provide per-layer cache geometry
KVCacheManagerV2->>config_utils: resolve mapped layer window
ModelEngine->>config_utils: resolve per-layer KV-head counts
ModelEngine->>ModelEngine: set attention metadata ratio
Suggested reviewers: Merge Risk: 🔵 Low · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage | Docstring coverage is 58.70% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 46 functions across 9 files. | Write docstrings for the functions missing them to satisfy the coverage threshold. |
✅ Passed checks (4 passed)
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Check name Status Explanation Title check ✅ Passed The title follows the required NVBugs and type format and clearly summarizes the primary Gemma4 startup fix for non-SM100 GPUs. Description check ✅ Passed The description explains the issue, solution, scope, compatibility considerations, test coverage, and checklist status. It provides sufficient detail for review. Linked Issues check ✅ Passed Check skipped because no linked issues were found for this pull request. Out of Scope Changes check ✅ Passed Check skipped because no linked issues were found for this pull request.
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Inline comments:
In `@tensorrt_llm/_torch/pyexecutor/_util.py`:
- Around line 2269-2272: Update the enclosing function’s head_dim annotation
from Optional[int] to a type that supports both an integer and list[int],
preserving the existing scalar and per-layer list assignments.
In `@tests/unittest/_torch/modeling/test_modeling_gemma4.py`:
- Around line 2790-2791: Patch
tensorrt_llm._torch.attention_backend.utils.IS_FLASHINFER_AVAILABLE to True
around the backend-dispatch test containing get_attention_backend and the
expected_class assertion, ensuring FLASHINFER consistently resolves to the
intended backend regardless of the environment.
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tensorrt_llm/_torch/models/modeling_gemma4.pytensorrt_llm/_torch/modules/qk_norm_attention.pytensorrt_llm/_torch/pyexecutor/_util.pytensorrt_llm/_torch/pyexecutor/config_utils.pytensorrt_llm/_torch/pyexecutor/kv_cache_manager_v2.pytensorrt_llm/_torch/pyexecutor/model_engine.pytests/unittest/_torch/executor/test_kv_cache_estimation.pytests/unittest/_torch/executor/test_pytorch_model_engine.pytests/unittest/_torch/modeling/test_modeling_gemma4.py
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brnguyen2
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Approving — the comments below are optional touch-ups, not blockers.
The unit tests pin the new-schema behavior well (the strict config doubles that raise on ambiguous global reads are a good regression guard), but the attn_backend default flip on non-SM100f has no integration or test-list coverage — the PR's own notes say no test_lists/ entries changed. This routing regressed once already when #16214 changed the default out from under #17557; a mocked is_sm_100f unit test won't catch the next default-priority change either. Consider adding (or confirming there is) an L0/QA Gemma4 entry on non-SM100 hardware (e.g. l0_* for SM120/Hopper) as a follow-up.
Two small robustness items inline. Everything else checks out: I traced the tightened is_gemma4_hybrid model_type gate through the unified and mm models (both surface the text config via post_config before any caller runs), and the V2 sizing layer_indices construction matches _resolve_num_attention_layers exactly, including the max(.., 1) floor. The gemma4 branch of _create_kv_cache_manager now ignores the num_kv_heads/head_dim caller overrides, but those are only set by the cross-KV encoder-decoder path, which Gemma4 can't reach.
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Actionable comments posted: 1
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Inline comments:
In `@tests/unittest/_torch/modeling/test_modeling_gemma4.py`:
- Line 161: Add complete type annotations to the added functions in
tests/unittest/_torch/modeling/test_modeling_gemma4.py: annotate the name
parameter and return type of __getattribute__, annotate config_dict and the
ModelConfig return type of the function at lines 175-175, and annotate *args,
**kwargs, plus the None return type of the function at lines 1010-1010.
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📒 Files selected for processing (9)
tensorrt_llm/_torch/models/modeling_gemma4.pytensorrt_llm/_torch/modules/qk_norm_attention.pytensorrt_llm/_torch/pyexecutor/_util.pytensorrt_llm/_torch/pyexecutor/config_utils.pytensorrt_llm/_torch/pyexecutor/kv_cache_manager_v2.pytensorrt_llm/_torch/pyexecutor/model_engine.pytests/unittest/_torch/executor/test_kv_cache_estimation.pytests/unittest/_torch/executor/test_pytorch_model_engine.pytests/unittest/_torch/modeling/test_modeling_gemma4.py
🚧 Files skipped from review as they are similar to previous changes (8)
- tensorrt_llm/_torch/modules/qk_norm_attention.py
- tests/unittest/_torch/executor/test_pytorch_model_engine.py
- tensorrt_llm/_torch/pyexecutor/model_engine.py
- tensorrt_llm/_torch/models/modeling_gemma4.py
- tests/unittest/_torch/executor/test_kv_cache_estimation.py
- tensorrt_llm/_torch/pyexecutor/config_utils.py
- tensorrt_llm/_torch/pyexecutor/kv_cache_manager_v2.py
- tensorrt_llm/_torch/pyexecutor/_util.py
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Thanks for the fix. I checked the core change on 71d1dca; the rebase to 0d3d2a0 keeps the same patch:
- With Transformers 5.16.1 and the local gemma-4-{12B, 31B, 26B-A4B, E2B, E4B}-it configs, base fails while constructing
Gemma4ForCausalLMwithAmbiguousGlobalPerLayerAttributeError('head_dim'). Head builds all five, and the per-layer head_dim / KV heads / Q heads / sliding windows match HF'sper_layer_config[i]for every layer. - Through
apply_model_defaults_to_llm_args, the non-SM100f default now resolves to FLASHINFER forGemma4ForCausalLM,Gemma4ForConditionalGenerationandGemma4UnifiedForConditionalGeneration. SM100f stays on TRTLLM, external shared-KV MTP stays on FLASHINFER, and an explicitattn_backendstill wins.
A few non-blocking items are inline. The set_kv_cache_layer_specs comment is the only one with a reachable startup failure, although on an undocumented spec-decoding combination. The rest are small readability and test suggestions.
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Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
…tures Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
Publish per-layer KV geometry from the target model so shared-KV assistant construction preserves the shared target specs. Resolve estimation windows through the manager helper and apply temporary pool sizing before budgeting. Validated 34 targeted tests with CI dependencies and 3 model-construction tests with Transformers 5.16.1. Both new regression scenarios fail on the prior production code. Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
…nfig Store the published per-layer KV geometry on each ModelConfig instead of the extra_attrs registry that one-model drafters share with the target, so a drafter neither sees nor overwrites the target's specs. Also address the remaining review nits: clarify the Gemma4 backend-default and attention comments, let the backend decide sliding-layer RoPE fusion, describe max_num_heads_per_kv precisely, add a message to the KV-head count assert, and make the metadata ratio test discriminate against last-layer logic. Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
…ion=None Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
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10 participants
Dev Engineer Review
head_dimand KV-head counts for heterogeneous and legacy Transformers configurations.head_dimpasses directly toQKNormRoPEAttention.QA Engineer Review
tests/unittest/_torch/executor/test_kv_cache_estimation.pytests/unittest/_torch/executor/test_pytorch_model_engine.pytests/unittest/_torch/modeling/test_modeling_gemma4.pyDescription
NVBug 6700677 reports that Gemma4 Unified fails during startup with newer
Transformers releases. Transformers now exposes Gemma4's heterogeneous
attention geometry through
per_layer_configand rejects ambiguous globalreads of fields such as
head_dimandnum_key_value_heads.This PR:
configs while preserving compatibility with the older flat config schema;
QKNormRoPEAttention, avoiding temporary mutation of the shared modelconfig;
capacity estimation, and attention-metadata GQA sizing; and
H512 layers cannot fall back to native MMHA. The existing TRTLLM default on
SM100f and the FlashInfer metadata requirement for external shared-KV MTP
remain unchanged.
These changes belong in one PR because fixing only the initial Transformers
exception allows startup to proceed to an independent H512 MMHA failure on
non-SM100f hardware. The production and regression changes together establish
one complete Gemma4-startup fix.
The change is scoped to Gemma4, adds no dependencies or public API, and keeps
the existing datacenter-Blackwell policy unchanged. It restores the
non-SM100f routing previously established by #17557 after the default changed
in #16214.
Test Coverage
Workstation Edition:
10 passed, 6 subtests passed.cache sizing, and metadata GQA ratio 16.
configs.
non-SM100f paths, including external shared-KV MTP precedence.
120-real) source build and isolated install with Python/native hashprovenance.
google/gemma-4-12B-itstartup on RTX PRO 6000 with Transformers5.16.1: loaded all weights, completed warmup and CUDA Graph capture, reached
Application startup complete, and shut down cleanly.pre-commit run --files <changed files>using Python 3.12: all applicablehooks passed.
git diff --checkandpy_compilefor all changed files passed.PR Checklist
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