1.18 habana_main merge #307
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Signed-off-by: Chendi.Xue <[email protected]>
Signed-off-by: Chendi.Xue <[email protected]>
Signed-off-by: Chendi.Xue <[email protected]>
Signed-off-by: Chendi.Xue <[email protected]>
…lava-1.5 (#283) FILL IN THE PR DESCRIPTION HERE FIX #282 (*link existing issues this PR will resolve*) **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p> <ul> <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li> <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion. </li> </ul> <h3>Thank You</h3> <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p> </details>
…odule. (#250) Co-authored-by: Konrad Zawora <[email protected]>
Update documentation on support of fp8
Moved files from vllm/hpu to another public repo: https://github.com/HabanaAI/vllm-hpu-extension It can be installed with pip install git+https://github.com/HabanaAI/vllm-hpu-extension.git
After #252, HPUGraph capture takes much less memory, and we can reduce the memory reserved for HPUGraphs. On Llama3.1-8b-Instruct (G2), capturing 100% of prefill and decode graphs on BS=256 now takes 1.566 GB of HBM, which is far less than 40% (~30 GB) we reserve by default. This results in lots of unused (==wasted) memory, which could be used instead for more KV cache blocks.
The original code doesn't print the default value correctly INFO 09-17 00:06:07 habana_model_runner.py:95] VLLM_PROMPT_BS_BUCKET_MIN=1 (default:_**min**_) INFO 09-17 00:06:07 habana_model_runner.py:95] VLLM_PROMPT_BS_BUCKET_STEP=1 (default:_**step**_) INFO 09-17 00:06:07 habana_model_runner.py:95] VLLM_PROMPT_BS_BUCKET_MAX=1 (default:_**max**_) This change make it print the correct default value INFO 09-17 21:30:51 habana_model_runner.py:95] VLLM_PROMPT_BS_BUCKET_MIN=1 (default:_**1**_) INFO 09-17 21:30:51 habana_model_runner.py:95] VLLM_PROMPT_BS_BUCKET_STEP=4 (default:_**32**_) INFO 09-17 21:30:51 habana_model_runner.py:95] VLLM_PROMPT_BS_BUCKET_MAX=4 (default:_**64**_)
Fix blocks number calculation for Flat PA via adding empty table_block (#158)
Remove dummy seq metadata from loop for Flat PA fix
Add extra mark_step() on each decode layer to optimize the performance on Gaudi. Signed-off-by: Bob Zhu <[email protected]>
…ng (#297) fix bug: device_str in initialize_ray_cluster requires uppercase string w/o the bug fix, multi HPUs will encounter "ValueError: The number of required hpus exceeds the total number of available hpus in the placement group" error, as the device_str is not expected as uppercase, then available hpus always returns 0.
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