[None][feat] Support DeepSeek-V4 in layer_wise_benchmarks by ruodil · Pull Request #16774 · NVIDIA/TensorRT-LLM

ruodil · 2026-07-23T04:20:51Z

DeepSeek-V4's multi-head hyper-connection (mHC) decoder layers do not fit the generic single-layer harness in tools/layer_wise_benchmarks/runner.py:

create_kv_cache_manager: the is_mla branch omitted vocab_size, which DeepseekV4CacheManager.init requires (keyword-only, no default).
create_run_pack: V4 layers take the initial residual as hc_state shaped [num_tokens, hc_mult, hidden_size] and their MoE routing needs input_ids; both are now synthesized when the config exposes hc_mult.
run_pack(check=True): guarded the NaN/Inf/zero check with isinstance since mHC layers return an HCState, not a Tensor.

Non-V4 models are unaffected (all V4 paths gate on hc_mult / MLA class).

Usage for V4: --moe-backend DEEPGEMM and a --layer-indices slice that starts at layer 0 and covers both compress ratios (e.g. 0,1,2,3).

Dev Engineer Review

Added DeepSeek-V4 support in runner.py by:
- Passing vocab_size to the MLA KV-cache manager.
- Synthesizing hc_state and input_ids for mHC layers.
- Restricting NaN/Inf/zero checks to tensor outputs.
- Gating V4-specific behavior to preserve non-V4 model behavior.
V4 benchmarks require --moe-backend DEEPGEMM and layer indices beginning at 0 and covering both compression ratios, e.g. 0,1,2,3.
No public API or configuration-file changes were identified.

QA Engineer Review

No test changes.

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DeepSeek-V4's multi-head hyper-connection (mHC) decoder layers do not fit the generic single-layer harness in tools/layer_wise_benchmarks/runner.py: - create_kv_cache_manager: the is_mla branch omitted vocab_size, which DeepseekV4CacheManager.__init__ requires (keyword-only, no default). - create_run_pack: V4 layers take the initial residual as hc_state shaped [num_tokens, hc_mult, hidden_size] and their MoE routing needs input_ids; both are now synthesized when the config exposes hc_mult. - run_pack(check=True): guarded the NaN/Inf/zero check with isinstance since mHC layers return an HCState, not a Tensor. Non-V4 models are unaffected (all V4 paths gate on hc_mult / MLA class). Usage for V4: --moe-backend DEEPGEMM and a --layer-indices slice that starts at layer 0 and covers both compress ratios (e.g. 0,1,2,3). Signed-off-by: Ruodi Lu <ruodil@users.noreply.github.com>

coderabbitai · 2026-07-23T04:24:38Z

Walkthrough

The layer-wise benchmark runner now prepares DeepSeek-V4 hyper-connection inputs, conditionally validates tensor outputs, and passes vocabulary size when constructing MLA KV-cache managers.

Changes

Benchmark runner behavior

Layer / File(s)	Summary
DeepSeek-V4 input preparation `tensorrt_llm/tools/layer_wise_benchmarks/runner.py`	Benchmark pack creation reshapes hidden states for configured `hc_mult` values and adds `input_ids` for MoE routing.
Validation and MLA cache wiring `tensorrt_llm/tools/layer_wise_benchmarks/runner.py`	Output checks now require tensor-valued hidden states, and MLA KV-cache manager construction receives `config.vocab_size`.

Estimated code review effort: 2 (Simple) | ~10 minutes

Suggested reviewers: dc3671

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🧹 Nitpick comments (1)

tensorrt_llm/tools/layer_wise_benchmarks/runner.py (1)
664-678: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Add a regression test for the V4 input contract.

Cover hc_mult=4 and a non-V4 configuration, asserting the expanded hidden-state shape and generated input_ids dtype/range. DeepSeek-V4 relies on expanded mHC residual streams and token-ID-based hash routing. (nvidia.github.io)
🤖 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/tools/layer_wise_benchmarks/runner.py` around lines 664 - 678,
Add a regression test covering the input-preparation logic around the hc_mult
handling, using hc_mult=4 and a non-V4 configuration. Assert that V4 hidden
states expand to [num_tokens, 4, hidden_size], generated input_ids use
torch.int32 and remain within [0, vocab_size), while the non-V4 path preserves
the original hidden-state shape and does not add input_ids.

🤖 Prompt for all review comments with 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.

Nitpick comments:
In `@tensorrt_llm/tools/layer_wise_benchmarks/runner.py`:
- Around line 664-678: Add a regression test covering the input-preparation
logic around the hc_mult handling, using hc_mult=4 and a non-V4 configuration.
Assert that V4 hidden states expand to [num_tokens, 4, hidden_size], generated
input_ids use torch.int32 and remain within [0, vocab_size), while the non-V4
path preserves the original hidden-state shape and does not add input_ids.

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Reviewing files that changed from the base of the PR and between 0a401b4 and 263b526.

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tensorrt_llm/tools/layer_wise_benchmarks/runner.py

ruodil · 2026-07-23T05:58:14Z

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ruodil · 2026-07-24T05:43:29Z

/bot run --disable-fail-fast

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ruodil requested a review from a team as a code owner July 23, 2026 04:20

ruodil requested review from dc3671 and nv-ananjappa July 23, 2026 04:20

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ruodil requested review from chenfeiz0326 and hyukn July 23, 2026 04:21

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Merge branch 'main' into user/ruodil/lwb-deepseek-v4-support

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hyukn approved these changes Jul 24, 2026

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[None][feat] Support DeepSeek-V4 in layer_wise_benchmarks#16774

[None][feat] Support DeepSeek-V4 in layer_wise_benchmarks#16774
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