[None][fix] Fix cache buffer size for window

[chuangz0]

Summary by CodeRabbit

• New Features
  • Buffer pre-allocation now aligns to tokens-per-block for more accurate and efficient memory usage.
• Refactor
  • Updated public API to require a tokens_per_block parameter for pre-allocation utilities across native and Python interfaces.
  • Internal calculations now cap and align window sizes to token blocks for consistent sizing.
• Tests
  • Unit tests updated to reflect the new parameter and sizing behavior.

Description

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[chuangz0]

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📝 Walkthrough

Walkthrough

The change adds a tokens-per-block parameter to preAllocBufferSize and updates all call sites/bindings. Buffer size computation now aligns window size to token blocks, caps by max tokens, and may add an extra block when misaligned. Tests and both C++ and Python bindings are adjusted accordingly.

Changes

Cohort / File(s)	Summary
Core buffer sizing logic and API cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h, cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp	Added size_t tokensPerBlock to preAllocBufferSize signature. Implemented alignedWindowSize and validTokenNum calculation with block alignment, max token cap, and optional extra block when window is misaligned. Propagated new parameter usage internally.
C++ caller update cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp	Updated preAllocBufferSize invocation to pass mModelConfig.getTokensPerBlock(), reorder args accordingly.
C++ bindings (nanobind) cpp/tensorrt_llm/nanobind/batch_manager/cacheTransceiver.cpp	Exposed new tokens_per_block as first argument in binding for preAllocBufferSize; updated argument order.
C++ bindings (pybind11) cpp/tensorrt_llm/pybind/batch_manager/cacheTransceiver.cpp	Added tokens_per_block parameter first in the pybind11 binding for preAllocBufferSize; adjusted signature order.
Python wrapper tensorrt_llm/_torch/pyexecutor/kv_cache_transceiver.py	Changed CacheTransBufferManager.pre_alloc_buffer_size signature to (tokens_per_block, kv_cache_size_per_token, cache_transceiver_config); forwarded new arg order to C++ binding.
Unit tests cpp/tests/unit_tests/batch_manager/cacheTransBufferTest.cpp	Updated calls to preAllocBufferSize to include tokensPerBlock as first argument; assertions rely on new buffer size behavior.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  actor Caller as Model/Executor
  participant CTBM as CacheTransBufferManager
  Caller->>CTBM: preAllocBufferSize(tokensPerBlock, cacheSizeMap, config)
  activate CTBM
  note right of CTBM: Compute alignedWindowSize = align(windowSize, tokensPerBlock)
  CTBM->>CTBM: validTokenNum = min(alignedWindowSize (+extra block if misaligned), maxNumTokens)
  CTBM-->>Caller: preAllocatedBytes
  deactivate CTBM
  Caller->>Caller: Configure transfer/alloc sizes

Loading

sequenceDiagram
  autonumber
  actor Py as Python Client
  participant PyWrap as Python CacheTransBufferManager
  participant PB as pybind/nanobind
  participant CTBM as C++ CacheTransBufferManager
  Py->>PyWrap: pre_alloc_buffer_size(tokens_per_block, kv_size, config)
  PyWrap->>PB: pre_alloc_buffer_size(tokens_per_block, map, config)
  PB->>CTBM: preAllocBufferSize(tokensPerBlock, map, config)
  CTBM-->>PB: bytes
  PB-->>PyWrap: bytes
  PyWrap-->>Py: bytes

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

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✅ Passed checks (1 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The title follows the required ticket and type format and concisely describes the main change of fixing the cache buffer sizing logic for window alignment, which reflects the core modifications made in this PR.

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Actionable comments posted: 4

🧹 Nitpick comments (1)

cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h (1)

63-65: Add Doxygen comment for tokensPerBlock
In cacheTransBuffer.h above the preAllocBufferSize declaration, add a brief Doxygen description of the tokensPerBlock parameter and how it influences buffer alignment and headroom.

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📥 Commits

Reviewing files that changed from the base of the PR and between d145e87 and 7d6c795.

📒 Files selected for processing (7)

• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp (3 hunks)
• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h (1 hunks)
• cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp (1 hunks)
• cpp/tensorrt_llm/nanobind/batch_manager/cacheTransceiver.cpp (1 hunks)
• cpp/tensorrt_llm/pybind/batch_manager/cacheTransceiver.cpp (1 hunks)
• cpp/tests/unit_tests/batch_manager/cacheTransBufferTest.cpp (2 hunks)
• tensorrt_llm/_torch/pyexecutor/kv_cache_transceiver.py (1 hunks)

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🧠 Learnings (4)

📓 Common learnings

Learnt from: thorjohnsen
PR: NVIDIA/TensorRT-LLM#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.

📚 Learning: 2025-08-14T21:04:50.248Z

Learnt from: thorjohnsen
PR: NVIDIA/TensorRT-LLM#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:

• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h
• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp
• tensorrt_llm/_torch/pyexecutor/kv_cache_transceiver.py
• cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp
• cpp/tensorrt_llm/pybind/batch_manager/cacheTransceiver.cpp
• cpp/tensorrt_llm/nanobind/batch_manager/cacheTransceiver.cpp
• cpp/tests/unit_tests/batch_manager/cacheTransBufferTest.cpp

📚 Learning: 2025-08-15T06:46:54.897Z

Learnt from: eopXD
PR: NVIDIA/TensorRT-LLM#6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:54.897Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp addToken function, newly allocated blocks are unshared by design. The beam search path in addToken (when sequence.getNumTokens() > windowSize) is currently broken/non-functional with SWA, so the block allocation doesn't follow a shared-then-unshared pattern.

Applied to files:

• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h
• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp
• cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp
• cpp/tensorrt_llm/pybind/batch_manager/cacheTransceiver.cpp
• cpp/tensorrt_llm/nanobind/batch_manager/cacheTransceiver.cpp
• cpp/tests/unit_tests/batch_manager/cacheTransBufferTest.cpp

📚 Learning: 2025-08-20T06:56:02.889Z

Learnt from: eopXD
PR: NVIDIA/TensorRT-LLM#6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:577-579
Timestamp: 2025-08-20T06:56:02.889Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, maxSequenceLength is now enforced as a non-optional argument in the BlockManager constructor, so concerns about std::nullopt defaulting to 0 are not applicable. When windowSize > maxSequenceLength, a warning should be added instead of handling optional parameter cases.

Applied to files:

• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h
• cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp
• cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp

🧬 Code graph analysis (4)

cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h (1)

cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp (2)

• preAllocBufferSize (244-279)
• preAllocBufferSize (244-246)

cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp (1)

cpp/tests/unit_tests/multi_gpu/cacheTransceiverTest.cpp (1)

• maxNumTokens (293-307)

tensorrt_llm/_torch/pyexecutor/kv_cache_transceiver.py (2)

tensorrt_llm/llmapi/llm_args.py (1)

• CacheTransceiverConfig (1277-1294)

cpp/tensorrt_llm/executor/cacheTransceiverConfig.cpp (1)

• CacheTransceiverConfig (24-29)

cpp/tests/unit_tests/batch_manager/cacheTransBufferTest.cpp (1)

cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp (2)

• preAllocBufferSize (244-279)
• preAllocBufferSize (244-246)

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🔇 Additional comments (5)

cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp (1)

309-310: LGTM: tokensPerBlock forwarded correctly.

Call matches new API and uses mModelConfig.getTokensPerBlock().

cpp/tensorrt_llm/pybind/batch_manager/cacheTransceiver.cpp (1)

181-183: Binding arg order aligned with C++ API.

Exposes tokens_per_block first; consistent with header/impl.

cpp/tensorrt_llm/nanobind/batch_manager/cacheTransceiver.cpp (1)

185-187: Nanobind signature updated correctly.

tokens_per_block added as first arg; consistent across bindings.

cpp/tests/unit_tests/batch_manager/cacheTransBufferTest.cpp (2)

117-118: Tests updated for new API — OK.

Now passing tokensPerBlock first; assertions still valid.

---

159-160: Tests updated for new API — OK.

Consistent with header/impl change.

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