[https://nvbugs/5474169][fix]Adjust max seq len for kvcache for memory estimation

[HuiGao-NV]

kv cache manager

Summary by CodeRabbit

• Bug Fixes

  • Fixed max sequence length handling during lazy generation by syncing executor and KV cache manager and deferring dummy request creation to avoid incorrect token limits.

• Chores

  • Enhanced GPU-memory logging for token calculations, now reporting computed max tokens with free/total memory context to aid troubleshooting.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

Lazy-init dummy requests in KvCacheCreator, store provided net_max_seq_len, and sync max_seq_len from the created KV cache manager back into KvCacheCreator and executor_config when applicable. Update a resource manager warning to show computed max_tokens and GPU free/total memory values.

Changes

Cohort / File(s)	Summary
KV cache lazy init & max_seq_len sync tensorrt_llm/_torch/pyexecutor/_util.py	Remove pre-creation of dummy context requests; store net_max_seq_len in an internal field; lazily create _dummy_reqs in _get_token_num_for_estimation; after creating KV cache manager, propagate manager.max_seq_len into self._max_seq_len and set executor_config.max_seq_len when the manager is active.
Logging message update tensorrt_llm/_torch/pyexecutor/resource_manager.py	Change warning message in calculate_max_num_blocks to report the computed max_tokens and include GPU free/total memory context; no control-flow or calculation changes.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  actor Caller
  participant K as KvCacheCreator
  participant M as KVCacheManager
  participant E as ExecutorConfig

  Caller->>K: _get_token_num_for_estimation()
  alt _dummy_reqs is None
    K->>K: create _dummy_reqs using (self._max_seq_len - 1)
  end
  K-->>Caller: return token estimate

  Caller->>K: _create_kv_cache_manager()
  K->>M: construct manager (may update manager.max_seq_len)
  M-->>K: return manager
  note right of M: SWA or internal logic may adjust max_seq_len
  alt manager is active
    K->>E: set E.max_seq_len = M.max_seq_len
  end
  K->>K: set self._max_seq_len = M.max_seq_len (if manager not None)

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

Possibly related PRs

• [https://nvbugs/5481385][fix] Fix max_seq_len in cuda graph warmup and intermediate_size in fused_moe_deepgemm #7345 — Related changes around clamping warmup token length / sequence-length handling for CUDA-graph/dummy warmup.

Suggested reviewers

• achartier
• yechank-nvidia
• omera-nv
• hlu1

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• tensorrt_llm/_torch/pyexecutor/_util.py (3 hunks)

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• tensorrt_llm/_torch/pyexecutor/_util.py

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[HuiGao-NV]

/bot run

[tensorrt-cicd]

PR_Github #17059 [ run ] triggered by Bot

[coderabbitai]

Actionable comments posted: 2

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

tensorrt_llm/_torch/pyexecutor/_util.py (1)

163-171: Fix double-counting of draft/spec tokens in estimation.

spec_cfg.max_draft_len is added twice when overlap scheduler is enabled, inflating KV token estimation.

-        num_extra_tokens_per_seq = num_extra_tokens_per_seq + 1
-            if spec_cfg is not None:
-                num_extra_tokens_per_seq += spec_cfg.max_draft_len
+        num_extra_tokens_per_seq += 1
+        # account for scheduler overlap; draft tokens added once below
         ...
-        if spec_cfg is not None:
-            num_extra_tokens_per_seq += spec_cfg.max_draft_len
-            num_extra_tokens_per_seq += get_num_extra_kv_tokens(spec_cfg)
+        if spec_cfg is not None:
+            # add draft tokens once; add any extra KV tokens once
+            num_extra_tokens_per_seq += spec_cfg.max_draft_len
+            num_extra_tokens_per_seq += get_num_extra_kv_tokens(spec_cfg)

🧹 Nitpick comments (4)

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

1-1: Add missing NVIDIA copyright header (2025).

Per repo guidelines, prepend the standard NVIDIA header to all source files.

+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.

---

10-13: Deduplicate imports (prefer one canonical path).

ModelConfig and PyTorchConfig are imported twice via absolute and relative paths. Keep one (prefer absolute) to avoid confusion and shadowing.

-from ..model_config import ModelConfig
-from ..speculative import get_num_extra_kv_tokens, get_spec_decoder
-from .config import PyTorchConfig
+from ..speculative import get_num_extra_kv_tokens, get_spec_decoder

Also applies to: 21-24

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

1-1: Add missing NVIDIA copyright header (2025).

Per repo guidelines, prepend the standard NVIDIA header to all source files.

+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.

---

488-489: Log memory in GiB (correct divisor and clearer message).

1 << 32 is not GiB; use 1 << 30 (or 1024**3) and label units to avoid confusion.

-                logger.warning(
-                    f'Both free_gpu_memory_fraction and max_tokens are set (to {free_mem_fraction} and {max_tokens} with free memory {free_mem / (1 << 32)} of total memory {total_mem / (1<<32)}, respectively). The smaller value will be used.'
-                )
+                logger.warning(
+                    f'Both free_gpu_memory_fraction and max_tokens are set (to {free_mem_fraction} and {max_tokens}, '
+                    f'with free memory {free_mem / (1 << 30):.2f} GiB of total memory {total_mem / (1 << 30):.2f} GiB). '
+                    'The smaller value will be used.'
+                )

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📒 Files selected for processing (2)

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• tensorrt_llm/_torch/pyexecutor/_util.py

🧠 Learnings (2)

📚 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:

• tensorrt_llm/_torch/pyexecutor/resource_manager.py

📚 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:

• tensorrt_llm/_torch/pyexecutor/resource_manager.py

🧬 Code graph analysis (1)

tensorrt_llm/_torch/pyexecutor/_util.py (1)

tensorrt_llm/_torch/attention_backend/trtllm.py (2)

• max_seq_len (561-571)
• max_seq_len (574-578)

⏰ 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/pyexecutor/_util.py (1)

53-55: LGTM: switch to lazy init and track net max_seq_len.

Storing net_max_seq_len and deferring dummy request creation is a good direction for avoiding upfront allocations.

[tensorrt-cicd]

PR_Github #17059 [ run ] completed with state SUCCESS
/LLM/release-1.0/L0_MergeRequest_PR pipeline #335 completed with status: 'SUCCESS'
Pipeline passed with automatic retried tests. Check the rerun report for details.

[HuiGao-NV]

/bot reuse-pipeline

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PR_Github #17165 [ reuse-pipeline ] completed with state SUCCESS
Reusing PR_Github #17059 for commit 339807f