[None][feat] Return logprobs incrementally in torch backend #8785
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📝 WalkthroughWalkthroughThese changes modify log probability handling in the executor system. In Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes
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🧹 Nitpick comments (1)
tensorrt_llm/_torch/pyexecutor/llm_request.py (1)
329-332: Preserve the Optional return contract forlog_probs.The new guard prevents the AttributeError, but short-circuiting now returns
Falsebefore initialization. Callers expect either the list orNone, so a bare bool makes the return type lie and can break downstream usage. ReturningNoneexplicitly keeps the Optional semantics intact.- return self._log_probs and hasattr( - self._log_probs, 'log_probs') and self._log_probs.log_probs + if not self._log_probs or not hasattr(self._log_probs, "log_probs"): + return None + return self._log_probs.log_probs
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tensorrt_llm/_torch/pyexecutor/llm_request.py(3 hunks)tensorrt_llm/executor/result.py(1 hunks)
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🧠 Learnings (2)
📚 Learning: 2025-08-28T10:25:22.370Z
Learnt from: ixlmar
PR: NVIDIA/TensorRT-LLM#7294
File: tensorrt_llm/_torch/pyexecutor/sampler.py:887-891
Timestamp: 2025-08-28T10:25:22.370Z
Learning: In tensorrt_llm/_torch/pyexecutor/sampler.py, the draft_probs and target_probs tensors have shapes [1, steps] not [steps, vocab_size] as might be expected, making the .squeeze(0) operations appropriate for removing the batch dimension of size 1.
Applied to files:
tensorrt_llm/executor/result.py
📚 Learning: 2025-08-19T12:45:11.997Z
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#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.
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🧬 Code graph analysis (1)
tensorrt_llm/executor/result.py (2)
tensorrt_llm/scaffolding/task.py (1)
logprobs(101-102)tensorrt_llm/_torch/pyexecutor/llm_request.py (1)
log_probs(329-331)
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Signed-off-by: Dong Cao <docao@nvidia.com>
Signed-off-by: Dong Cao <docao@nvidia.com>
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Overall LGTM. Is it correct that this issue is flaky and cannot be captured for unittesting in test_llm_return_logprobs_streaming_tp2() and related tests ?
This is not a matter of correctness, but a performance issue. Before this PR, in streaming scenarios, each returned response contained the full logprobs, which significantly impacted the latency of streaming output. |
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