[https://nvbugs/5767223][feat] add pp support for DeepSeek-v3.2

[lfr-0531]

Summary by CodeRabbit

• Bug Fixes
  • Improved sparse attention layer indexing behavior by removing overly restrictive layer configuration constraints.
  • Fixed layer index mapping during generation and context handling to use local layer indices for more accurate top-k index transformations.

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Description

GPQA accuracy:

lm-eval gpqa_diamond_cot_zeroshot_aa results (scores normalized to range 0~100):
|           Tasks            |Version|   Filter   |n-shot|  Metric   |   | Value |   |Stderr|
|----------------------------|------:|------------|-----:|-----------|---|------:|---|-----:|
|gpqa_diamond_cot_zeroshot_aa|      1|strict-match|     0|exact_match|↑  |82.3232|±  |2.7179|

Test Coverage

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📝 Walkthrough

Walkthrough

This change modifies the sparse attention backend by removing a num_layers validation assertion and updating the sparse_attn_predict method to use a dynamically obtained local layer index via get_local_layer_idx(metadata) instead of the static self.layer_idx when transforming top-k indices.

Changes

Cohort / File(s)	Summary
Sparse Attention Layer Index Handling tensorrt_llm/_torch/attention_backend/sparse/dsa.py	Removed assertion validating num_layers == kv_cache_manager.num_local_layers; updated sparse_attn_predict to pass local layer index via self.get_local_layer_idx(metadata) instead of self.layer_idx to transform_local_topk_and_prepare_pool_view, altering how global indices are mapped for the current layer during generation or context processing.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description is incomplete. It lacks explicit explanation of what changes were made and why, includes only evaluation results, and the 'Test Coverage' section is empty with no test cases referenced.	Add a clear 'Description' section explaining the pp support changes made to the sparse attention backend, and list specific test cases or test files in the 'Test Coverage' section.
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly indicates the main change: adding pipeline parallelism (pp) support for DeepSeek-v3.2, which aligns with the actual code changes in the sparse attention backend.

✨ Finishing touches

• 📝 Generate docstrings

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📥 Commits

Reviewing files that changed from the base of the PR and between 6507087 and f5ab24a.

📒 Files selected for processing (1)

• tensorrt_llm/_torch/attention_backend/sparse/dsa.py

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

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Files:

• tensorrt_llm/_torch/attention_backend/sparse/dsa.py

**/*.{cpp,cc,cxx,h,hpp,hxx,cu,cuh,py}

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Files:

• tensorrt_llm/_torch/attention_backend/sparse/dsa.py

🧠 Learnings (3)

📚 Learning: 2025-08-15T06:46:53.813Z

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:53.813Z
Learning: In the TensorRT-LLM KV cache manager, SWA (Sliding Window Attention) combined with beam search is currently in a broken/non-functional state and is planned for future rework. During preparatory refactoring phases, code related to SWA+beam search may intentionally remain in a non-working state until the broader rework is completed.

Applied to files:

• tensorrt_llm/_torch/attention_backend/sparse/dsa.py

📚 Learning: 2025-08-19T12:45:11.997Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.

Applied to files:

• tensorrt_llm/_torch/attention_backend/sparse/dsa.py

📚 Learning: 2025-08-14T21:04:50.248Z

Learnt from: thorjohnsen
Repo: NVIDIA/TensorRT-LLM PR: 6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.

Applied to files:

• tensorrt_llm/_torch/attention_backend/sparse/dsa.py

🧬 Code graph analysis (1)

tensorrt_llm/_torch/attention_backend/sparse/dsa.py (1)

tensorrt_llm/_torch/attention_backend/trtllm.py (1)

• get_local_layer_idx (1546-1550)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (1)

tensorrt_llm/_torch/attention_backend/sparse/dsa.py (1)

1649-1649: No changes needed at line 1649.

The usage of self.get_local_layer_idx(metadata) at line 1649 is correct and intentional. The apparent inconsistency with lines 1239, 1289, and 1469 is not a concern:

• get_indexer_k_cache_buffers() expects a global layer_idx and internally applies layer_offsets[layer_idx] to compute the local offset
• transform_local_topk_and_prepare_pool_view() expects a local layer index (already offset-adjusted) because it uses stride_factor = num_layers * tokens_per_block for a layer-interleaved KV cache layout

Different methods have different index type expectations by design. The change to use get_local_layer_idx(metadata) at line 1649 is the correct approach for PP support.

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[lfr-0531]

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[lfr-0531]

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[lfr-0531]

I tested the accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus[baseline_pp4_mtp1] locally on B200, and it passed.