[TRTLLM-9086][doc] Clean up TODOs in documentation #9292
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Signed-off-by: junq <[email protected]>
📝 WalkthroughWalkthroughThis pull request removes TODO comments from three files and adds a new beam search demonstration function to the LLM sampling examples. The demonstration integrates with existing demonstration flows and supports a new "beam" command-line option. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes
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🧹 Nitpick comments (1)
examples/llm-api/llm_sampling.py (1)
140-156: Consider printing all beam outputs to better demonstrate beam search.The function sets
n=beam_width=2, which will generate 2 beam search outputs, but only prints the first one (line 155). Similar to howdemonstrate_multiple_sequences(lines 136-137) iterates through all outputs to showcase multiple sequences, this function would be more educational if it displayed all beam outputs to better demonstrate beam search results.Apply this diff to display all beam outputs:
response = llm.generate(prompt, sampling_params) print(f"Prompt: {prompt}") - print(f"Response: {response.outputs[0].text}") + for i, output in enumerate(response.outputs): + print(f"Beam {i+1}: {output.text}")
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📒 Files selected for processing (4)
cpp/include/tensorrt_llm/runtime/iTensor.h(0 hunks)docs/source/deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.md(0 hunks)examples/llm-api/llm_sampling.py(3 hunks)tensorrt_llm/functional.py(0 hunks)
💤 Files with no reviewable changes (3)
- cpp/include/tensorrt_llm/runtime/iTensor.h
- docs/source/deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.md
- tensorrt_llm/functional.py
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🧠 Learnings (1)
📓 Common learnings
Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.
🧬 Code graph analysis (1)
examples/llm-api/llm_sampling.py (2)
tensorrt_llm/_torch/pyexecutor/sampler.py (1)
beam_width(78-81)tensorrt_llm/_torch/pyexecutor/model_engine.py (1)
use_beam_search(418-419)
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🔇 Additional comments (3)
examples/llm-api/llm_sampling.py (3)
194-195: LGTM!The beam search demonstration is correctly integrated into the demonstration flow and receives the appropriate prompt parameter.
209-209: LGTM!The CLI integration for the beam search demonstration is complete and correct. The "beam" option is properly added to the choices and handled consistently with other demonstration options.
Also applies to: 239-240
221-221: LGTM!The example in the docstring is helpful and follows the established format, demonstrating the new beam search option effectively.
2 participants
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