[None][feat] Return topk logprobs in torch backend #7976
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PR_Github #19889 [ run ] triggered by Bot |
📝 WalkthroughWalkthroughAdds num_logprobs propagation from task/request layers into executor and LlmRequest, updates PyTorch sampler to compute per-token logprobs via top-k over log-softmaxed logits, removes a PyTorch-only validation limiting logprobs > 1, wires worker/scaffolding to pass logprobs, and extends tests to validate ordering/ranks. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant Client
participant LLMAPI as LLM API
participant Worker as BaseWorker
participant ExecReq as ExecutorRequest
participant LlmReq as LlmRequest (Py)
participant Sampler
Client->>LLMAPI: GenerationRequest(sampling_params.logprobs = N)
LLMAPI->>Worker: create request (no PyTorch logprobs cap)
Worker->>ExecReq: set py_num_logprobs = N
ExecReq->>LlmReq: construct(num_logprobs = py_num_logprobs)
note over LlmReq: Stores py_num_logprobs
LlmReq->>Sampler: sample(step logits, num_logprobs = N)
rect rgba(200, 235, 255, 0.2)
note right of Sampler: New/changed
Sampler->>Sampler: log_softmax(logits)
Sampler->>Sampler: top-k over log-probs (k = N)
Sampler->>Sampler: build Logprob entries (rank=1..N)
end
Sampler-->>Client: tokens + per-step top-k logprobs
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Possibly related PRs
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Actionable comments posted: 3
🧹 Nitpick comments (3)
tests/unittest/llmapi/test_llm.py (1)
1865-1876: Align size expectation with earlier check; remove k+1 inconsistency.Above (Line 1861), the test allows len(...) in {k, k+1}, but here it enforces exactly k. Pick one behavior to avoid flaky failures. If the sampler now guarantees exactly top‑k, assert k consistently in both places.
Apply this change outside the current hunk (near Line 1861) to make both checks consistent:
# Replace: assert logprobs_result and len(logprobs_result[0].keys()) in {logprobs, logprobs + 1} # With: assert logprobs_result and len(logprobs_result[0].keys()) == logprobstensorrt_llm/_torch/pyexecutor/llm_request.py (1)
314-315: Constructor surface looks good; consider documenting num_logprobs.Signature extension with
num_logprobs: int = 0is fine. Add a brief docstring entry so downstream readers know it controls top‑k logprobs size for generation.tensorrt_llm/scaffolding/worker.py (1)
183-185: Propagate prompt_logprobs as well (if available on task).You’re wiring
logprobsfor generation. IfGenerationTaskalso carries prompt‑time logprobs (mirroring SamplingParams(prompt_logprobs=...)), pass it through to support prompt logprobs in this worker path.For example:
sampling_params = SamplingParams( max_tokens=task.max_tokens, temperature=task.temperature, top_p=task.top_p, top_k=task.top_k, return_context_logits=task.return_context_logits, - logprobs=task.num_logprobs) + logprobs=task.num_logprobs, + prompt_logprobs=getattr(task, "prompt_logprobs", None))
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📒 Files selected for processing (7)
tensorrt_llm/_torch/pyexecutor/llm_request.py(3 hunks)tensorrt_llm/_torch/pyexecutor/sampler.py(7 hunks)tensorrt_llm/executor/base_worker.py(1 hunks)tensorrt_llm/llmapi/llm.py(0 hunks)tensorrt_llm/scaffolding/worker.py(1 hunks)tests/unittest/llmapi/test_llm.py(1 hunks)tests/unittest/llmapi/test_llm_pytorch.py(1 hunks)
💤 Files with no reviewable changes (1)
- tensorrt_llm/llmapi/llm.py
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🧠 Learnings (1)
📚 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/_torch/pyexecutor/sampler.py
🧬 Code graph analysis (3)
tensorrt_llm/executor/base_worker.py (1)
tensorrt_llm/scaffolding/task.py (1)
logprobs(99-100)
tensorrt_llm/scaffolding/worker.py (2)
tests/unittest/llmapi/test_llm.py (6)
task(481-488)task(528-533)task(1881-1890)task(2000-2013)task(2400-2401)task(2491-2510)tensorrt_llm/scaffolding/task.py (1)
logprobs(99-100)
tensorrt_llm/_torch/pyexecutor/sampler.py (2)
tensorrt_llm/executor/result.py (1)
Logprob(37-40)tensorrt_llm/_torch/pyexecutor/scheduler.py (1)
all_requests(38-39)
🪛 Ruff (0.13.1)
tensorrt_llm/_torch/pyexecutor/sampler.py
1276-1276: Unused method argument: log_probs_host
(ARG002)
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🔇 Additional comments (2)
tests/unittest/llmapi/test_llm_pytorch.py (1)
918-918: Good addition to cover top-k>1 in streaming path.This parametrization exercises the new top‑3 generation logprobs in the PyTorch backend under streaming. Looks consistent with the PR intent.
tensorrt_llm/_torch/pyexecutor/llm_request.py (1)
359-360: LGTM: carry num_logprobs on the Python side.Storing
py_num_logprobson the request mirrors other py_* fields and will propagate to child requests via the existing copy path.
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Signed-off-by: Dong Cao <docao@nvidia.com>
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