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sync with IBM/main #13
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Update dockerfile.ubi to build vllm using wheels! I had to update some `init` files since we need those packages to be picked up when building the wheel for vllm. ### Integration tests https://v3.travis.ibm.com/github/ai-foundation/fmaas-inference-server/builds/17962397 Image pushed to quay for testing: ``` quay.io/wxpe/tgis-vllm:release-vllm-wheel.eec7a7b ``` <img width="1020" alt="Screenshot 2024-04-23 at 12 18 00" src="https://github.com/IBM/vllm/assets/9909241/f261bc38-d1f9-4d1a-a5d6-9db14aa362a6"> Useful command to build the above tests: ``` env: global: - REMOTE_INTEGRATION_TESTS=true - REMOTE_INTEGRATION_TEST_IMAGE=quay.io/wxpe/tgis-vllm:release-vllm-wheel.eec7a7b - REMOTE_INTEGRATION_TEST_CONFIG=product.vllm ``` --- <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> --------- Signed-off-by: Prashant Gupta <prashantgupta@us.ibm.com>
Signed-off-by: Nick Hill <nickhill@us.ibm.com> Co-authored-by: Daniel Clark <daniel.clark@ibm.com>
…3974) [Bugfix] Fix CustomAllreduce pcie nvlink topology detection (vllm-project#3974) (vllm-project#4159)
…/crash in distributed inference (vllm-project#4079) Co-authored-by: Simon Mo <simon.mo@hey.com>
Co-authored-by: simon-mo <simon.mo@hey.com>
Co-authored-by: Zhong Wang <wangzhong@infini-ai.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-13-147.ec2.internal>
Co-authored-by: Harry Mellor <hmellor@oxts.com>
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
Co-authored-by: Harry Mellor <hmellor@oxts.com>
…ct#3748) Co-authored-by: Yun Ding <yunding@nvidia.com> Co-authored-by: Roger Wang <ywang@roblox.com>
…d CI fixes and refactoring (vllm-project#4129)
…oject#3993) Signed-off-by: Tao He <sighingnow@gmail.com>
…he revision parameter (vllm-project#4217)
Co-authored-by: Harry Mellor <hmellor@oxts.com>
Co-authored-by: LiuXiaoxuanPKU <llilyliupku@gmail.com>
… Dynamic/Static Activations) (vllm-project#4527) Follow on to vllm-project#4332 to enable FP8 checkpoint loading for Mixtral and supersedes vllm-project#4436. This PR enables the following checkpoint loading features for Mixtral: Supports loading fp8 checkpoints for Mixtral, such as this "nm-testing/Mixtral-8x7B-Instruct-v0.1-FP8" test model Supports static or dynamic activation quantization with static weight quantization (all per tensor) Supports different scales for each expert weight Supports Fp8 in QKV layer Notes: The Expert Gate/Router always runs at half / full precision for now. If there are different weight scales between QKV layer (for separate QKV weights), they are re-quantized using layer.weight_scale.max() so we can have a single gemm for performance.
Signed-off-by: Joe Runde <Joseph.Runde@ibm.com>
@z103cb here's the current diff with IBM/main diff --git a/Dockerfile.ubi b/Dockerfile.ubi
index 452b3fa0..48e87808 100644
--- a/Dockerfile.ubi
+++ b/Dockerfile.ubi
@@ -116,7 +116,7 @@ RUN ldconfig /usr/local/cuda-12.2/compat/
## Python cuda base #################################################################
FROM cuda-devel as python-cuda-base
-COPY --from=python-install --link /opt/vllm /opt/vllm
+COPY --from=python-install /opt/vllm /opt/vllm
ENV PATH=/opt/vllm/bin/:$PATH
# install cuda and common dependencies
@@ -206,16 +206,16 @@ ENV PATH=/usr/local/cuda/bin:$PATH
ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# Copy the entire directory before building wheel
-COPY --link vllm vllm
+COPY vllm vllm
# Comment if building *.so files from scratch
##################################################
# Copy the prebuilt *.so files
-COPY --from=prebuilt-wheel --link /workspace/vllm/*.so /workspace/vllm/
+COPY --from=prebuilt-wheel /workspace/vllm/*.so /workspace/vllm/
##################################################
# Copy over the generated *.pb2 files
-COPY --from=gen-protos --link /workspace/vllm/entrypoints/grpc/pb vllm/entrypoints/grpc/pb
+COPY --from=gen-protos /workspace/vllm/entrypoints/grpc/pb vllm/entrypoints/grpc/pb
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
@@ -251,7 +251,7 @@ FROM cuda-runtime AS vllm-openai
WORKDIR /workspace
# Create release python environment
-COPY --from=python-cuda-base --link /opt/vllm /opt/vllm
+COPY --from=python-cuda-base /opt/vllm /opt/vllm
ENV PATH=/opt/vllm/bin/:$PATH
# install vllm wheel first, so that torch etc will be installed
diff --git a/vllm/entrypoints/grpc/grpc_server.py b/vllm/entrypoints/grpc/grpc_server.py
index 15885fca..ebec0bbc 100644
--- a/vllm/entrypoints/grpc/grpc_server.py
+++ b/vllm/entrypoints/grpc/grpc_server.py
@@ -92,7 +92,6 @@ class TextGenerationService(generation_pb2_grpc.GenerationServiceServicer):
self.engine: AsyncLLMEngine = engine
# These set in _post_init()
- self.tokenizer_group: BaseTokenizerGroup = None
self.tokenizer: Union[PreTrainedTokenizer,
PreTrainedTokenizerFast] = None
self.config: ModelConfig = None
@@ -101,9 +100,13 @@ class TextGenerationService(generation_pb2_grpc.GenerationServiceServicer):
self.skip_special_tokens = not args.output_special_tokens
self.default_include_stop_seqs = args.default_include_stop_seqs
+ @property
+ def tokenizer_group(self) -> BaseTokenizerGroup:
+ return self.engine.engine.tokenizer
+
+
async def _post_init(self):
self.config = await self.engine.get_model_config()
- self.tokenizer_group = await self.engine.get_tokenizer_group()
self.tokenizer = await self.engine.get_tokenizer()
# Swap in the special TGIS stats logger
|
/lgtm |
/approve |
z103cb
approved these changes
May 7, 2024
/approve |
[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: dtrifiro, z103cb The full list of commands accepted by this bot can be found here.
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prarit
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Oct 18, 2024
Initial mi300 fused_moe tuning using docker: pytorch-private:vllm0.3.3_ROCm6.2_pytorch2.3_hipblaslt0.7_v1
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sync with IBM/vllm@4c758aa2